Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Acquisition
2.2. Data Analysis
3. Input Data for ACM Roof Classification
3.1. Hyperspectral Imagery (HSI)
3.2. Multispectral Imagery (MSI)
3.2.1. Pan Sharpening of Satellite Imagery
3.2.2. Fusion of MSI with Light Detection and Ranging (LiDAR)
4. Classification Methods of ACM Roof Mapping
4.1. Pixel-Based Image Analysis (PBIA) Approach
4.2. Object-Based Image Analysis (OBIA) Approach
4.2.1. Image Segmentation in the OBIA Process
4.2.2. Image Classification in the OBIA Process
Supervised Classification
Supervised Rule-Based Classification
Fuzzy Rule-Based Classification (Expert System)
4.3. Deep-Learning-Based (DL-Based) Approach
5. Discussion
5.1. Summary of the Reviewed Studies
5.2. Major Challenges and Opportunities
- The low spatial resolution of early HSI was a significant challenge causing large OEs [13,20,21,22,23]. As explained in Section 4.1, a window of 3 × 3 image pixels is required to detect a roof unit with reasonable confidence [72]; consequently, for HSI with a 4 m spatial resolution, traditional pixel-based classifiers often were able to detect roof units with an area of larger than 144 m2 [22]. While large OEs were reported in early studies of ACM roof mapping using HSI, two studies [13,20] indicated high OAs. This was due to the large areas of the classified roof units, because the classification accuracy increases when roof units have large areas. In the study by Cilia et al. (2014) [13], roofs with an area smaller than 36 m2 were excluded from accuracy assessment, which resulted in high OA. Hence, the results of the above studies may not be consistent due to the high OEs of asbestos classes. Due to the advancement in remote sensors, low spatial resolution is not a challenge with current hyperspectral images, as they often provide both high spectral and spatial resolutions. Additionally, for low-resolution HSI, DL-based semantic classification could enhance the accuracy results by assigning class membership to subpixels instead of a single label [141,142,145].
- High dimensionality remained a challenge in ACM roof mapping studies, despite enhancement in the spatial resolution of recent HSI [23,24]. Using the non-optimal number of bands and features could adversely affect the classification accuracy (i.e., the curse of dimensionality) [34]. Furthermore, the high dimensionality of the data increases image processing time and causes inter-class confusion [35]. High dimensional space causes noisy maps due to the atypical or mixed pixels in pixel-based methods [36]. Dimensionality reduction techniques such as principal component analysis (PCA), and sequential forward selection (SFS) could significantly enhance the classification accuracy via optimal selection of features [152]. In the study by Szabo et al. (2014) [23], the PA of asbestos increased from 23% to 86.9% by reducing the dimensionality of another dataset. Several studies suggested OBIA methods could have a better performance in handling high dimensionality of HSI in comparison with PBIA classification methods [152,153]. Moreover, Hamedianfar et al. (2014) [24] indicated that integrating a data mining (DT) algorithm with OBIA classification could significantly improve attribute selection and classification accuracy.
- On the other hand, while very-high-resolution MSIs have been suitable input data, the pixel-based classification methods could not handle the detailed data of MSI with fine resolution [72]. The Earth′s surface is a mixture of various natural and artificial materials, so the finer resolution of MSI represents more details, resulting in complex spectral responses, particularly in heterogeneous urban areas [30]. Using spectral information in PBIA methods increases intra-class variability, confusion among classes, and salt-and-pepper errors when classifying very-high-resolution MSI [37,154]. Apart from that, other challenges may arise from interconnection among image resolutions, bands, and the cost of acquiring very high-resolution data in which using new technologies such as drones could reduce the survey costs [29]. As mentioned above, OBIA methods have been an appropriate alternative for classifying very high-resolution MSI [10,11,26,27,28,29,30,31,32]. However, misclassification errors were reported [27,29,32] as another challenge that was often connected to variability in the roof condition and geometry. In this regard, [30,32] showed that the fusion of MSI with LiDAR could not only significantly enhance the classification accuracy, but also could be capable of evaluating the ACM roof conditions.
- Generating the optimised segmentation parameters plays a significant role in achieving acceptable accuracy results in OBIA classifications. Both over-segmentation and under-segmentation [157] can result in too many small objects or large objects corresponding to mixed classes. A trial-and-error approach was adopted in ACM roof mapping studies [29] to identify the optimal parameters of objects; however, this segmentation process is subjective, which may cause poor results [155]. Apart from that, ENVI tools were utilised [28,30] for the segmentation of images, while no method was adopted to evaluate the segmentation quality. Several techniques for segmentation optimisation are proven to enhance the results, reduce the processing time, and minimise trial efforts [48,158]. A Taguchi optimisation technique, which has been widely used in OBIA studies, showed acceptable results [32,47,130,155]. In other remote sensing applications, DL-based image segmentation methods such as Mask R-CNN have shown good performance [45]. Moreover, it is suggested that the collaboration of segmentation with classification [115] could identify more accurate objects by adding a segmentation step in the classification process [41,42,107].
- The reliance on training data was the main barrier to transferability and widespread use of supervised classification methods [111,112]. The size, representativeness, cost, and sample collection strategies could significantly affect the classification accuracy [117]. When training samples are gathered from field surveys, the availability of ground references and the geophysical situation of study areas raise more challenges, particularly in complex and heterogeneous urban areas [110,118]. Moreover, because of the differences in inherent attributes among study areas [40], it is usually required to develop a new dataset for transferring the model to other areas, which is a limitation of autonomous ACM roof classification over wide-scale study sites. While defining training data requires several factors to consider, Mather et al. (2011) [118] suggested that the sample size should preferably be 30 times greater than the number of RSI spectral bands used in classification. Furthermore, adopting advanced non-parametric classifiers such as RF and SVM could have an acceptable performance when a small number of training data are available [46]. Supervised classifications were often outperformed by rule-based methods in the reviewed studies of ACM roof mapping [10,32]. Hence, fuzzy rule-based (expert systems) could be an alternative when adequate training data are not available.
- Analysts’ subjectivity in expert systems could cause biased and error-prone results because the rules are defined based on the analyst′s knowledge and reasoning on feature classes [101,105,153,156]. Consequently, when rulesets are developed by different operators, different results may be achieved. In the ACM roof mapping studies, while [10,28,29,30] reported acceptable accuracy results of rule-based classification, a systematic evaluation of the subjectivity [101] is missing. In this regard, adopting automatic induction (i.e., data mining) methods could be a suitable solution to reduce the effects of the analyst′s subjectivity [24,65,120]. Moreover, within the end-to-end deep learning structure, feature extraction is replaced by feature learning as a part of the classifier training phase. In this case, instead of defining the inner steps of the feature engineering phase, the end-to-end architecture generalises the model generation involving feature learning as part of it [49]. Hence, DL-based end-to-end architectures have been suggested as a practical approach to replace conventional rule-based OBIA classifications.
6. Conclusions and Future Direction
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sen, D. Working with asbestos and the possible health risks. Occup. Med. 2015, 65, 6–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Snabe, L.; Candscientsanpubl, N.; Baelum, J.; Rasmussen, J.; Dahl, S.; Olsen, K.E.; Albin, M.; Hansen, N.C.; Sherson, D. Occupational asbestos exposure and lung cancer—a systematic review of the literature. Arch. Environ. Occup. Health 2014, 69, 191–206. [Google Scholar] [CrossRef]
- Currie, G.P.; Watt, S.J.; Maskell, N.A. An overview of how asbestos exposure affects the lung. BMJ 2009, 339, b3209. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Doll, R.; Peto, J. Effects on Health of Exposure to Asbestos; Health & Safety Commission: London, UK, 1985. [Google Scholar]
- Banks, D.E.; Wang, M.-l.; Parker, J.E. Asbestos exposure, asbestosis, and lung cancer. Chest 1999, 115, 320–322. [Google Scholar] [CrossRef] [PubMed]
- LaDou, J. The asbestos cancer epidemic. Environ. Health Perspect. 2004, 112, 285–290. [Google Scholar] [CrossRef] [Green Version]
- Campopiano, A.; Ramires, D.; Zakrzewska, M.; Ferri, R.; Annibale, A.D.; Pizzutelli, G. Risk Assessment of the Decay of Asbestos Cement Roofs. Ann. Occup. Hyg. 2009, 53, 627–638. [Google Scholar] [CrossRef]
- Bloise, A.; Catalano, M.; Teresa, C.; Carmine, A.; Domenico, M. Naturally occurring asbestos: Potential for human exposure, San Severino Lucano (Basilicata, Southern Italy). Environ. Earth Sci. 2017, 76, 648. [Google Scholar] [CrossRef]
- Zen, I.S.; Ahamad, R.; Rampal, K.G.; Omar, W.; Safitri, I.; Ahamad, R.; Rampal, K.G.; Omar, W.; Zen, I.S.; Ahamad, R.; et al. Use of asbestos building materials in Malaysia: Legislative measures, the management, and recommendations for a ban on use. Int. J. Occup. Environ. Health 2013, 19, 169–178. [Google Scholar] [CrossRef]
- Gibril, M.B.A.; Shafri, H.Z.M.; Hamedianfar, A. New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images. Int. J. Remote Sens. 2017, 38, 467–491. [Google Scholar] [CrossRef]
- Tommasini, M.; Bacciottini, A.; Gherardelli, M. A QGIS Tool for Automatically Identifying Asbestos Roofing. ISPRS Int. J. Geo-Inf. 2019, 8, 131. [Google Scholar] [CrossRef] [Green Version]
- Krówczyńska, M.; Raczko, E.; Staniszewska, N.; Wilk, E. Asbestos-cement roofing identification using remote sensing and convolutional neural networks (CNNs). Remote Sens. 2020, 12, 408. [Google Scholar] [CrossRef] [Green Version]
- Cilia, C.; Panigada, C.; Rossini, M.; Candiani, G.; Pepe, M.; Colombo, R. Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images. ISPRS Int. J. Geo-Inf. 2015, 4, 928–941. [Google Scholar] [CrossRef] [Green Version]
- Virta, R.L. Asbestos: Geology, Mineralogy, Mining, and Uses; USGS: Washington, DC, USA, 2002.
- Krówczyńska, M.; Wilk, E. Aerial imagery and geographic information systems used in the asbestos removal process in Poland. In Proceedings of the 33rd EARSeL Symposium “Towards Horizon 2020: Earth Observation and Social Perspectives”, Matera, Italy, 3–6 June 2013. [Google Scholar]
- Samsudin, S.H.; Shafri, H.Z.; Hamedianfar, A. Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data. J. Appl. Remote Sens. 2016, 10, 025021. [Google Scholar] [CrossRef]
- Bhaskaran, S.; Paramananda, S.; Ramnarayan, M. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Appl. Geogr. 2010, 30, 650–665. [Google Scholar] [CrossRef]
- Taherzadeh, E.; Shafri, H.Z.M.; Shahi, K. Roof material detection based on object-based approach using WorldView-2 satellite imagery. J. Comput. Electr. Autom. Control. Inf. Eng. 2014, 8, 1737–1740. [Google Scholar]
- Manolakis, D.; Shaw, G. Detection algorithms for hyperspectral imaging applications. IEEE Signal Process. Mag. 2002, 19, 29–43. [Google Scholar] [CrossRef]
- Fiumi, L.; Campopiano, A.; Casciardi, S.; Ramires, D. Method validation for the identification of asbestos–cement roofing. Appl. Geomat. 2012, 4, 55–64. [Google Scholar] [CrossRef]
- Fiumi, L.; Congedo, L.; Meoni, C. Developing expeditious methodology for mapping asbestos-cement roof coverings over the territory of Lazio Region. Appl. Geomat. 2014, 6, 37–48. [Google Scholar] [CrossRef]
- Frassy, F.; Candiani, G.; Rusmini, M.; Maianti, P.; Marchesi, A.; Nodari, F.R.; Via, G.D.; Albonico, C.; Gianinetto, M. Mapping asbestos-cement roofing with hyperspectral remote sensing over a large mountain region of the Italian western alps. Sensors 2014, 14, 15900–15913. [Google Scholar] [CrossRef] [Green Version]
- Szabó, S.; Burai, P.; Kovács, Z.; Szabó, G.; Kerényi, A.; Fazekas, I.; Paládi, M.; Buday, T.; Szabó, G. Testing algorithms for the identification of asbestos roofing based on hyperspectral data. Environ. Eng. Manag. J. 2014, 143, 2875–2880. [Google Scholar] [CrossRef]
- Hamedianfar, A.; Shafri, H.Z.M.; Mansor, S.; Ahmad, N. Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images. J. Appl. Remote Sens. 2014, 8, 085091. [Google Scholar] [CrossRef]
- Camps-Valls, G.; Tuia, D.; Gómez-Chova, L.; Jiménez, S.; Malo, J. Remote Sensing Image Processing. Synth. Lect. Image Video Multimed. Process. 2011, 12, 1–194. [Google Scholar] [CrossRef]
- De Pinho, C.M.D.; Fonseca, L.M.G.; Korting, T.S.; de Almeida, C.M.; Kux, H.J.H. Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis. Int. J. Remote Sens. 2012, 33, 5973–5995. [Google Scholar] [CrossRef]
- Taherzadeh, E.; Shafri, H.Z.M. Development of a generic model for the detection of roof materials based on an object-based approach using WorldView-2 satellite imagery. Adv. Remote Sens. 2013, 2, 312–321. [Google Scholar] [CrossRef] [Green Version]
- Hamedianfar, A.; Shafri, H.Z.M. Detailed intra-urban mapping through transferable OBIA rule sets using WorldView-2 very-high-resolution satellite images. Int. J. Remote Sens. 2015, 36, 3380–3396. [Google Scholar] [CrossRef]
- Hamedianfar, A.; Shafri, H.Z.M. Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery. Geocarto Int. 2014, 29, 268–292. [Google Scholar] [CrossRef]
- Hamedianfar, A.; Shafri, H.Z.M.; Mansor, S.; Ahmad, N. Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data. Int. J. Remote Sens. 2014, 35, 1876–1899. [Google Scholar] [CrossRef]
- Abriha, D.; Kovács, Z.; Ninsawat, S.; Bertalan, L.; Balázs, B.; Szabó, S. Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery—A comparison. Hung. Geogr. Bull. 2018, 67, 375–392. [Google Scholar] [CrossRef] [Green Version]
- Norman, M.; Shafri, H.Z.M.; Mansor, S.; Yusuf, B.; Radzali, N.A.W.M. Fusion of multispectral imagery and LiDAR data for roofing materials and roofing surface conditions assessment. Int. J. Remote Sens. 2020, 41, 7090–7111. [Google Scholar] [CrossRef]
- Richards, J.A.; Richards, J.A. Remote Sensing Digital Image Analysis; Springer: Berlin/Heidelberg, Germany, 1999. [Google Scholar]
- Hughes, G. On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 1968, 14, 55–63. [Google Scholar] [CrossRef] [Green Version]
- Kavzoglu, T.; Mather, P.M. The role of feature selection in artificial neural network applications. Int. J. Remote Sens. 2002, 23, 2919–2937. [Google Scholar] [CrossRef]
- Hountondji, Y.C.; Sokpon, N.; Ozer, P. Analysis of the vegetation trends using low resolution remote sensing data in Burkina Faso (1982–1999) for the monitoring of desertification. Int. J. Remote Sens. 2006, 27, 871–884. [Google Scholar] [CrossRef] [Green Version]
- Neupane, B.; Horanont, T.; Aryal, J. Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis. Remote Sens. 2021, 13, 808. [Google Scholar] [CrossRef]
- Blaschke, T.; Strobl, J. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. Z. Geoinf. 2001, 14, 12–17. [Google Scholar]
- Aggarwal, N.; Dutta, M. Comparative Analysis of Pixel-Based and Object-Based Classification of High Resolution Remote Sensing Images—A Review. Int. J. Eng. Trends Technol. 2016, 38, 5–11. [Google Scholar] [CrossRef]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef]
- Hossain, M.D.; Chen, D. Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS J. Photogramm. Remote Sens. 2019, 150, 115–134. [Google Scholar] [CrossRef]
- Guo, Z.; Du, S. Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data. GIScience Remote Sens. 2016, 54, 38–63. [Google Scholar] [CrossRef]
- Tremeau, A.; Borel, N. A region growing and merging algorithm to color segmentation. Pattern Recognit. 1997, 30, 1191–1203. [Google Scholar] [CrossRef]
- Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58, 239–258. [Google Scholar] [CrossRef]
- Minaee, S.; Boykov, Y.Y.; Porikli, F.; Plaza, A.J.; Kehtarnavaz, N.; Terzopoulos, D. Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 2021, 44, 3523–3542. [Google Scholar] [CrossRef] [PubMed]
- Kavzoglu, T. Increasing the accuracy of neural network classification using refined training data. Environ. Model. Softw. 2009, 24, 850–858. [Google Scholar] [CrossRef]
- Gibril, M.B.A.; Idrees, M.O.; Yao, K.; Shafri, H.Z.M. Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data. J. Appl. Remote Sens. 2018, 12, 016036. [Google Scholar] [CrossRef]
- Drǎguţ, L.; Csillik, O.; Eisank, C.; Tiede, D. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogramm. Remote Sens. 2014, 88, 119–127. [Google Scholar] [CrossRef] [Green Version]
- Vali, A.; Comai, S.; Matteucci, M. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sens. 2020, 12, 2495. [Google Scholar] [CrossRef]
- González-Audícana, M.; Otazu, X.; Fors, O.; Alvarez-Mozos, J. A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1683–1690. [Google Scholar] [CrossRef]
- Grant, M.J.; Booth, A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Inf. Libr. J. 2009, 26, 91–108. [Google Scholar] [CrossRef]
- Shafri, H.Z.M. Machine Learning in Hyperspectral and Multispectral Remote Sensing Data Analysis. In Artificial Intelligence Science and Technology, Proceedings of the 2016 International Conference on Artificial Intelligence Science and Technology (AIST2016), Shanghai, China, 15–17 July 2016; World Scientific: Singapore, 2016; pp. 3–9. [Google Scholar] [CrossRef]
- Zhou, W.; Newsam, S.; Li, C.; Shao, Z. Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval. Remote Sens. 2017, 9, 489. [Google Scholar] [CrossRef] [Green Version]
- Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sens. 2013, 5, 1704–1733. [Google Scholar] [CrossRef] [Green Version]
- Braun, A.; Warth, G.; Bachofer, F.; Hochschild, V. Identification of roof materials in high-resolution multispectral images for urban planning and monitoring. In Proceedings of the 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France, 22–24 May 2019. [Google Scholar] [CrossRef]
- Li, X.; Zhou, Y. Urban mapping using DMSP/OLS stable night-time light: A review. Taylor Fr. 2017, 38, 6030–6046. [Google Scholar] [CrossRef]
- Berberoglu, S.; Akin, A. Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. Int. J. Appl. Earth Obs. Geoinf. 2009, 11, 46–53. [Google Scholar] [CrossRef]
- Castagno, J.; Atkins, E. Roof shape classification from LiDAR and satellite image data fusion using supervised learning. Sensors 2018, 18, 3960. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, B.; Zhang, Y.; Wang, S. A lightweight and discriminative model for remote sensing scene classification with multidilation pooling module. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 2636–2653. [Google Scholar] [CrossRef]
- Cheng, G.; Xie, X.; Han, J.; Guo, L.; Xia, G.-S. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 3735–3756. [Google Scholar] [CrossRef]
- Chen, Y.; Zhou, Y.; Ge, Y.; An, R.; Chen, Y. Enhancing land cover mapping through integration of pixel-based and object-based classifications from remotely sensed imagery. Remote Sens. 2018, 10, 77. [Google Scholar] [CrossRef] [Green Version]
- Pohl, C.; Van Genderen, J.L. Review article Multisensor image fusion in remote sensing: Concepts, methods and applications. Int. J. Remote Sens. 2010, 19, 823–854. [Google Scholar] [CrossRef] [Green Version]
- Dadrass, F.; Samadzadegan, F.; Mehravar, S.; Toosi, A.; Khatami, R.; Stein, A. A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS J. Photogramm. Remote Sens. 2021, 171, 101–117. [Google Scholar] [CrossRef]
- Yuhendra; Alimuddin, I.; Sumantyo, J.T.S.; Kuze, H. Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 165–175. [Google Scholar] [CrossRef]
- Hodgson, M.E.; Jensen, J.R.; Tullis, J.A.; Riordan, K.D.; Archer, C.M. Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness. Photogramm. Eng. Remote Sens. 2003, 69, 973–980. [Google Scholar] [CrossRef] [Green Version]
- Zhou, G. Seamless fusion of LiDAR and aerial imagery for building extraction. IEEE Trans. Geosci. Remote Sens. 2014, 52, 7393–7407. [Google Scholar] [CrossRef]
- Yu, Q.; Gong, P.; Tian, Y.Q.; Pu, R.; Yang, J. Factors affecting spatial variation of classification uncertainty in an image object-based vegetation mapping. Photogramm. Eng. Remote Sens. 2008, 74, 1007–1018. [Google Scholar] [CrossRef] [Green Version]
- Li, M.; Ma, L.; Blaschke, T.; Cheng, L.; Tiede, D. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. Int. J. Appl. Earth Obs. Geoinf. 2016, 49, 87–98. [Google Scholar] [CrossRef]
- Li, P.; Xu, H.; Li, S. Urban impervious surface extraction from very high resolution imagery by one-class support vector machine. In Proceedings of the ISPRS TC VII Symposium—100 Years ISPRS, Vienna, Austria, 5–7 July 2010. [Google Scholar]
- Cowen, D.J.; Jensen, J.R.; Bresnahan, P.J.; Ehler, G.B.; Graves, D. The design and implementation of an integrated geographic information system for environmental applications. Photogramm. Eng. Remote Sens. 1995, 61, 1393–1404. [Google Scholar]
- Jensen, J.R.; Im, J. Remote sensing change detection in urban environments. In Geo-Spatial Technologies in Urban Environments; Springer: Berlin/Heidelberg, Germany, 2007; pp. 7–31. [Google Scholar]
- Myint, S.W.; Gober, P.; Brazel, A.; Grossman-clarke, S.; Weng, Q. Per-pixel vs. object-based classi fi cation of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 2011, 115, 1145–1161. [Google Scholar] [CrossRef]
- Lin, S.-K. Introduction to Remote Sensing. Fifth Edition. By James B. Campbell and Randolph H. Wynne, The GuilfordPress, 2011; 662 pages. Price: £80.75, ISBN 978-1-60918-176-5. Remote Sens. 2013, 5, 282–283. [Google Scholar] [CrossRef] [Green Version]
- Wu, Z. Superpixel-based unsupervised change detection using multi-dimensional change vector analysis and SVM-based classification. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, I-7, 257–262. [Google Scholar] [CrossRef] [Green Version]
- Hay, G.J.; Castilla, G. Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In Object-Based Image Analysis; Springer: Berlin/Heidelberg, Germany, 2008; pp. 75–89. [Google Scholar]
- Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.; Queiroz Feitosa, R.; van der Meer, F.; van der Werff, H.; van Coillie, F.; et al. Geographic Object-Based Image Analysis—Towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 2014, 87, 180–191. [Google Scholar] [CrossRef] [Green Version]
- Bioucas-Dias, J.M.; Plaza, A.; Camps-Valls, G.; Scheunders, P.; Nasrabadi, N.M.; Chanussot, J. Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. 2013, 1, 6–36. [Google Scholar] [CrossRef] [Green Version]
- Navulur, K. Multispectral Image Analysis Using the Object-Oriented Paradigm; CRC Press: Boca Raton, FL, USA, 2006; pp. 1–155. [Google Scholar]
- Zhang, Y.J. Evaluation and comparison of different segmentation algorithms. Pattern Recognit. Lett. 1997, 18, 963–974. [Google Scholar] [CrossRef]
- Neubert, M.; Herold, H.; Meinel, G. Assessing image segmentation quality–concepts, methods and application. In Object-Based Image Analysis; Springer: Berlin/Heidelberg, Germany, 2008; pp. 769–784. [Google Scholar] [CrossRef]
- Su, T.; Zhang, S. Local and global evaluation for remote sensing image segmentation. ISPRS J. Photogramm. Remote Sens. 2017, 130, 256–276. [Google Scholar] [CrossRef]
- Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011, 66, 247–259. [Google Scholar] [CrossRef]
- Pal, N.R.; Pal, S.K. A review on image segmentation techniques. Pattern Recognit. 1993, 26, 1277–1294. [Google Scholar] [CrossRef]
- Costa, H.; Foody, G.M.; Boyd, D.S. Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sens. Environ. 2018, 205, 338–351. [Google Scholar] [CrossRef] [Green Version]
- Kavzoglu, T.; Tonbul, H. A comparative study of segmentation quality for multi-resolution segmentation and watershed transform. In Proceedings of the 8th International Conference on Recent Advances in Space Technologies, RAST 2017, Istanbul, Turkey, 19–22 June 2017; pp. 113–117. [Google Scholar] [CrossRef]
- Tautz, D. Segmentation. Dev. Cell 2004, 7, 301–312. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhou, Y.T.; Venkateswar, V.; Chellappa, R. Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11, 84–95. [Google Scholar] [CrossRef]
- Lu, Y.; Jain, R.C. Behavior of Edges in Scale Space. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11, 337–356. [Google Scholar] [CrossRef]
- Pong, T.C.; Shapiro, L.G.; Watson, L.T.; Haralick, R.M. Experiments in segmentation using a facet model region grower. Comput. Vis. Graph. Image Process. 1984, 25, 1–23. [Google Scholar] [CrossRef]
- Cao, W.; Li, J.; Liu, J.; Zhang, P. Two improved segmentation algorithms for whole cardiac CT sequence images. In Proceedings of the 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Datong, China, 15–17 October 2016. [Google Scholar]
- Shih, F.Y.; Cheng, S. Adaptive mathematical morphology for edge linking. Inf. Sci. 2004, 167, 9–21. [Google Scholar] [CrossRef]
- Martin, D.R.; Fowlkes, C.C.; Malik, J. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 530–549. [Google Scholar] [CrossRef] [PubMed]
- Jin, X.; Paswaters, S. A fuzzy rule base system for object-based feature extraction and classification. In Proceedings of the Defense and Security Symposium, Orlando, FL, USA, 9–13 April 2007; Volume 6567, pp. 547–558. [Google Scholar] [CrossRef]
- Guindon, B. Computer-Based Aerial Image Understanding: A Review and Assessment of its Application to Planimetric Information Extraction from Very High Resolution Satellite Images. Can. J. Remote Sens. 2014, 23, 38–47. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Sorjonen-ward, P.; Ord, A.; Southgate, P.N. Numerical Modelling of Fluid Flow During Structural Closure of the Isa Superbasin in the Lawn Hill Platform, Northern Australia. ASEG Ext. Abstr. 2019, 2006, 1–5. [Google Scholar] [CrossRef]
- Ohta, Y.I.; Kanade, T.; Sakai, T. Color information for region segmentation. Comput. Graph. Image Process. 1980, 13, 222–241. [Google Scholar] [CrossRef]
- Fan, J.; Yau, D.K.Y.; Elmagarmid, A.K.; Aref, W.G. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans. Image Process. 2001, 10, 1454–1466. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Adams, R.; Bischof, L. Seeded Region Growing. IEEE Trans. Pattern Anal. Mach. Intell. 1994, 16, 641–647. [Google Scholar] [CrossRef] [Green Version]
- Kim, M.; Warner, T.A.; Madden, M.; Atkinson, D.S. Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: Scale, texture and image objects. Int. J. Remote Sens. 2011, 32, 2825–2850. [Google Scholar] [CrossRef]
- Smith, A. Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm. J. Spat. Sci. 2010, 55, 69–79. [Google Scholar] [CrossRef]
- Belgiu, M.; Drǎguţ, L.; Strobl, J. Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS J. Photogramm. Remote Sens. 2014, 87, 205–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hussain, M.; Chen, D.; Cheng, A.; Wei, H.; Stanley, D. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 2013, 80, 91–106. [Google Scholar] [CrossRef]
- Arbiol, R.; Zhang, Y.; Comellas, V.P. Advanced classification techniques: A review. Rev. Catalana Geogr. 2007, 12, 1–4. [Google Scholar]
- Drǎguţ, L.; Eisank, C. Automated object-based classification of topography from SRTM data. Geomorphology 2012, 141–142, 21–33. [Google Scholar] [CrossRef] [Green Version]
- Martha, T.R.; Kerle, N.; van Westen, C.J.; Jetten, V.; Vinod Kumar, K. Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS J. Photogramm. Remote Sens. 2012, 67, 105–119. [Google Scholar] [CrossRef]
- Johnson, B.; Xie, Z. Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogramm. Remote Sens. 2011, 66, 473–483. [Google Scholar] [CrossRef]
- Hadavand, A.; Saadatseresht, M.; Homayouni, S. Segmentation parameter selection for object-based land-cover mapping from ultra high resolution spectral and elevation data. Int. J. Remote Sens. 2017, 38, 3586–3607. [Google Scholar] [CrossRef]
- Tseng, M.H.; Chen, S.J.; Hwang, G.H.; Shen, M.Y. A genetic algorithm rule-based approach for land-cover classification. ISPRS J. Photogramm. Remote Sens. 2008, 63, 202–212. [Google Scholar] [CrossRef]
- Marino, C.M.; Panigada, C.; Busetto, L. Airborne hyperspectral remote sensing applications in urban areas: Asbestos concrete sheeting identification and mapping. In Proceedings of the IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No. 01EX482), Rome, Italy, 8–9 November 2001; pp. 212–216. [Google Scholar]
- Campbell, J.B.; Wynne, R.H. Introduction to Remote Sensing; Guilford Press: New York, NY, USA, 2011. [Google Scholar]
- Ma, L.; Li, M.; Ma, X.; Cheng, L.; Du, P.; Liu, Y. A review of supervised object-based land-cover image classification. ISPRS J. Photogramm. Remote Sens. 2017, 130, 277–293. [Google Scholar] [CrossRef]
- Ma, L.; Cheng, L.; Li, M.; Liu, Y.; Ma, X. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery. ISPRS J. Photogramm. Remote Sens. 2015, 102, 14–27. [Google Scholar] [CrossRef]
- Rougier, S.; Puissant, A.; Stumpf, A.; Lachiche, N. Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images. Int. J. Appl. Earth Obs. Geoinf. 2016, 51, 60–73. [Google Scholar] [CrossRef]
- Laliberte, A.S.; Browning, D.M.; Rango, A. A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery. Int. J. Appl. Earth Obs. Geoinf. 2012, 15, 70–78. [Google Scholar] [CrossRef]
- Whiteside, T.G.; Maier, S.W.; Boggs, G.S. Area-based and location-based validation of classified image objects. Int. J. Appl. Earth Obs. Geoinf. 2014, 28, 117–130. [Google Scholar] [CrossRef]
- Lu, D.; Weng, Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 2007, 28, 823–870. [Google Scholar] [CrossRef]
- Chen, D.; Stow, D. The effect of training strategies on supervised classification at different spatial resolutions. Photogramm. Eng. Remote Sens. 2002, 68, 1155–1162. [Google Scholar]
- Mather, P.M.; Koch, M. Computer Processing of Remotely-Sensed Images: An Introduction; John Wiley & Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
- Hubert-Moy, L.; Cotonnec, A.; Le Du, L.; Chardin, A.; Perez, P. A Comparison of Parametric Classification Procedures of Remotely Sensed Data Applied on Different Landscape Units. Remote Sens. Environ. 2001, 75, 174–187. [Google Scholar] [CrossRef]
- Weng, Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens. Environ. 2012, 117, 34–49. [Google Scholar] [CrossRef]
- Pax-Lenney, M.; Woodcock, C.E.; Macomber, S.A.; Gopal, S.; Song, C. Forest mapping with a generalized classifier and Landsat TM data. Remote Sens. Environ. 2001, 77, 241–250. [Google Scholar] [CrossRef]
- Albert, T.H. Evaluation of Remote Sensing Techniques for Ice-Area Classification Applied to the Tropical Quelccaya Ice Cap, Peru. Polar Geogr. 2011, 26, 210–226. [Google Scholar] [CrossRef]
- BOLSTAD, P.; Lillesand, T. Rule-based classification models- Flexible integration of satellite imagery and thematic spatial data. Photogramm. Eng. Remote Sens. 1992, 58, 965–971. [Google Scholar]
- Lawrence, R.L.; Wright, A. Rule-based classification systems using classification and regression tree (CART) analysis. Photogramm. Eng. Remote Sens. 2001, 67, 1137–1142. [Google Scholar]
- Hansen, M.; Dubayah, R.; Defries, R. Classification trees: An alternative to traditional land cover classifiers. Int. J. Remote Sens. 2007, 17, 1075–1081. [Google Scholar] [CrossRef]
- Gu, X.; Angelov, P.P. Semi-supervised deep rule-based approach for image classification. Appl. Soft Comput. J. 2018, 68, 53–68. [Google Scholar] [CrossRef] [Green Version]
- Pal, M.; Mather, P.M. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 2003, 86, 554–565. [Google Scholar] [CrossRef]
- Borak, J.S. Feature selection and land cover classification of a MODIS-like data set for a semiarid environment. Int. J. Remote Sens. 1999, 20, 919–938. [Google Scholar] [CrossRef]
- Roe, B.P.; Yang, H.J.; Zhu, J.; Liu, Y.; Stancu, I.; McGregor, G. Boosted decision trees as an alternative to artificial neural networks for particle identification. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2005, 543, 577–584. [Google Scholar] [CrossRef] [Green Version]
- Hamedianfar, A.; Barakat, A.; Gibril, M. Large-scale urban mapping using integrated geographic object-based image analysis and artificial bee colony optimization from worldview-3 data. Int. J. Remote Sens. 2019, 40, 6796–6821. [Google Scholar] [CrossRef]
- Xu, H. Rule-based impervious surface mapping using high spatial resolution imagery. Int. J. Remote Sens. 2012, 34, 27–44. [Google Scholar] [CrossRef]
- Kohli, D.; Warwadekar, P.; Kerle, N.; Sliuzas, R.; Stein, A. Transferability of Object-Oriented Image Analysis Methods for Slum Identification. Remote Sens. 2013, 5, 4209–4228. [Google Scholar] [CrossRef] [Green Version]
- Donnay, J.-P.; Barnsley, M.; Longley, P. Remote Sensing and Urban Analysis—Introduction; CRC Press: Boca Raton, FL, USA, 2001. [Google Scholar]
- Hofmann, P.; Blaschke, T.; Strobl, J. Quantifying the robustness of fuzzy rule sets in object-based image analysis. Int. J. Remote Sens. 2011, 32, 7359–7381. [Google Scholar] [CrossRef]
- Stumpf, A.; Kerle, N. Object-oriented mapping of landslides using Random Forests. Remote Sens. Environ. 2011, 115, 2564–2577. [Google Scholar] [CrossRef]
- Abunadi, I.; Albraikan, A.A.; Alzahrani, J.S.; Eltahir, M.M.; Hilal, A.M.; Eldesouki, M.I.; Motwakel, A.; Yaseen, I. An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification. Healthcare 2022, 10, 697. [Google Scholar] [CrossRef]
- Bacanin, N.; Zivkovic, M.; Al-Turjman, F.; Venkatachalam, K.; Trojovský, P.; Strumberger, I.; Bezdan, T. Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application. Sci. Rep. 2022, 12, 6302. [Google Scholar] [CrossRef]
- Pan, S.J.; Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345–1359. [Google Scholar] [CrossRef]
- Wang, H.; Wang, Y.; Zhang, Q.; Xiang, S.; Pan, C. Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images. Remote Sens. 2017, 9, 446. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.; Piramanayagam, S.; Monteiro, S.T.; Saber, E. Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields. J. Appl. Remote Sens. 2019, 13, 016501. [Google Scholar] [CrossRef] [Green Version]
- Panboonyuen, T.; Jitkajornwanich, K.; Lawawirojwong, S.; Srestasathiern, P.; Vateekul, P. Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning. Remote Sens. 2019, 11, 83. [Google Scholar] [CrossRef] [Green Version]
- Ji, S.; Wei, S.; Lu, M. A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery. Int. J. Remote Sens. 2018, 40, 3308–3322. [Google Scholar] [CrossRef]
- Mou, L.; Zhu, X.X. RiFCN: Recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images. arXiv 2018, arXiv:1805.02091. [Google Scholar]
- Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 3431–3440. [Google Scholar]
- Wang, F. Fuzzy Supervised Classification of Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 1990, 28, 194–201. [Google Scholar] [CrossRef] [Green Version]
- Yang, L.; Xian, G.; Klaver, J.M.; Deal, B. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogramm. Eng. Remote Sens. 2003, 69, 1003–1010. [Google Scholar] [CrossRef]
- Deng, C.; Wu, C. A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sens. Environ. 2013, 133, 62–70. [Google Scholar] [CrossRef]
- Rasti, B.; Hong, D.; Hang, R.; Ghamisi, P.; Kang, X.; Chanussot, J.; Benediktsson, J.A. Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep: Overview and Toolbox. IEEE Geosci. Remote Sens. Mag. 2020, 8, 60–88. [Google Scholar] [CrossRef]
- Zou, Q.; Ni, L.; Zhang, T.; Wang, Q. Deep Learning Based Feature Selection for Remote Sensing Scene Classification. IEEE Geosci. Remote Sens. Lett. 2015, 12, 2321–2325. [Google Scholar] [CrossRef]
- Han, W.; Feng, R.; Wang, L.; Cheng, Y. A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification. ISPRS J. Photogramm. Remote Sens. 2018, 145, 23–43. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, H.; Xue, X.; Jiang, Y.; Shen, Q. Deep learning for remote sensing image classification: A survey. WIREs Data Min. Knowl. Discov. 2018, 8, e1264. [Google Scholar] [CrossRef] [Green Version]
- Kavzoglu, T.; Tonbul, H.; Yildiz Erdemir, M.; Colkesen, I. Dimensionality Reduction and Classification of Hyperspectral Images Using Object-Based Image Analysis. J. Indian Soc. Remote Sens. 2018, 46, 1297–1306. [Google Scholar] [CrossRef]
- Duro, D.C.; Franklin, S.E.; Dubé, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 2012, 118, 259–272. [Google Scholar] [CrossRef]
- Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65, 2–16. [Google Scholar] [CrossRef] [Green Version]
- Norman, M.; Mohd Shafri, H.Z.; Idrees, M.O.; Mansor, S.; Yusuf, B. Spatio-statistical optimization of image segmentation process for building footprint extraction using very high-resolution WorldView 3 satellite data. Geocarto Int. 2020, 35, 1124–1147. [Google Scholar] [CrossRef]
- Arvor, D.; Durieux, L.; Andrés, S.; Laporte, M.-A. Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective. ISPRS J. Photogramm. Remote Sens. 2013, 82, 125–137. [Google Scholar] [CrossRef]
- Marpu, P.R.; Neubert, M.; Herold, H.; Niemeyer, I. Enhanced evaluation of image segmentation results. J. Spat. Sci. 2010, 55, 55–68. [Google Scholar] [CrossRef]
- Martha, T.R.; Kerle, N.; Van Westen, C.J.; Jetten, V.; Kumar, K.V. Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans. Geosci. Remote Sens. 2011, 49, 4928–4943. [Google Scholar] [CrossRef]
Imagery | Spectral Channels | Spatial Resolution | Research Purpose |
---|---|---|---|
MIVIS | 102 channels from 433 to 1270 nm | 4–9 m | Mapping ACM roofing [22] |
4 m | Mapping ACM roofing [20] | ||
3 m | Mapping ACM roofing [13,21] | ||
AISA Eagle II | 126 channels from 400 to 1000 nm | 1 m | Mapping ACM roofing [23] |
AISA Classic | 20 channels from 400 to 970 nm | 1 m | Mapping intra-urban land cover, including one ACM class [24] |
AISA Eagle | 128 channels from 400 to 970 nm | 0.68 m | Mapping intra-urban land cover, including one ACM class [24] |
Imagery | Imagery Resolution | Band Composition | Research Purpose |
---|---|---|---|
WV-2 | Panchromatic: 0.5 m Multispectral: 1.85 m | Eight bands | Mapping ACM roofing [10] |
Mapping roofing materials, including one ACM class [27,31] | |||
Mapping impervious and pervious surfaces, including one ACM class [29] | |||
Mapping intra-urban land cover including three ACM classes [28] | |||
Eight bands Fusion with LiDAR | Mapping intra-urban land cover, including one ACM class [30] | ||
WV-3 | Panchromatic: 0.31 m Multispectral: 1.24 m | Eight bands | Mapping ACM roofing [11] |
Eight bands Fusion with LiDAR | Mapping roofing materials, including two ACM classes [32] | ||
Aerial imagery | Spatial: 0.25 m | RGB and CIR | Mapping ACM roofing [12] |
IKONOS II | Panchromatic: 1 m Multispectral: 4 m | Four bands (Blue, Green, Red, and Near-infrared) | Mapping intra-urban land cover including three ACM classes [26] |
Authors | Classification/Segmentation Method | Classifier | Accuracy Result % |
---|---|---|---|
Fiumi et al. (2012) [20] | PBIA | Spectral Angle Mapper (SAM) | OA: 88, PA (ACM class): 94.3 |
Fiumi et al. (2014) [21] | PBIA | Spectral Angle Mapper (SAM) | OA: 65 |
Frassy et al. (2014) [22] | PBIA | Spectral Angle Mapper (SAM) | OA: 80, PA (ACM class): 43 |
Szabó et al. (2014) [23] | PBIA | Spectral Angle Mapper (SAM), Support Vector Machine (SVM) | SVM OA: 79.9 SAM OA: 59.8 |
Krowczynska et al. (2020) [12] | Pixel-based DL | Convolutional Neural Networks (CNN) | OA: 89, PA: 89, UA: 88 (ACM class) |
Abriha et al. (2018) [31] | PBIA | Discriminant Function Analysis (DFA), Random Forest (RF) | OA: 85 |
Tommasini et al. (2019) [11] | PBIA | Random Forest (RF) | OA: 82 |
Authors | Imagery Type | Segmentation | Classification | Accuracy Result % |
---|---|---|---|---|
Pinho et al. (2012) [26] | IKONOS II | Region-based segmentation (MRS) | Supervised Decision Tree (DT) | OA: 71.91 PA (ACM class): 38–90 |
Taherizade et al. (2013) [27] | WV-2 | Multiscale Edge-based segmentation (ENVI Feature Extraction tool) | Supervised (Maximum Likelihood) Rule based Per pixel | Supervised OA: 46, Rule-based OA: 82, PA (ACM class): 80, 95 |
Hamedianfar et al. (2014) [29] | WV-2 | Edge-based segmentation (ENVI Feature Extraction tool) | Supervised (SVM) Rule based Per pixel | Supervised OA: 82.80, Rule-based OA: 85–87, PBIA OA: 73–77 |
Hamedianfar et al. (2014) [30] | Fusion of WV-2 with LiDAR | Edge-based segmentation (ENVI Feature Extraction tool) | Rule based | OA: 92.84, PA: 81, UA: 93 (ACM class) |
Hamedianfar et al. (2014) [24] | AISA Classic | Edge-based segmentation (ENVI Feature Extraction tool) | Supervised (DT) | OA: 93.42, PA and UA (ACM class): 100 |
Hamedianfar et al. (2015) [28] | WV-2 | Edge-based segmentation (ENVI Feature Extraction tool) | Rule-based | OA: 88, PA: 96, UA: 84 (ACM class) |
Gibril et al. (2017) [10] | WV-2 | Optimised Region-based segmentation (MRS) by Taguchi optimisation technique | Supervised (Bayes, k-NN, SVM, and RF) Rule based | Supervised OA: 72–82, Rule-based OA: 90–93 |
Norman et al. (2020) [32] | Fusion of WV-3 and LiDAR | Optimised Region-based segmentation (MRS) by Taguchi optimisation technique | Supervised (SVM, DT) | SVM OA: 70, DT: 87 |
Authors | Imagery Type | Sensor | Classification Approach | Classifier | Highest Accuracy Result % |
---|---|---|---|---|---|
Fiumi et al., 2012 [20] | HSI | MIVIS | PBIA | SAM | OA: 88 PA: 94 |
Fiumi et al., 2014 [21] | HSI | MIVIS | PBIA | SAM | OA: 65 |
Frassy et al., 2014 [22] | HSI | MIVIS | PBIA | SAM | OA 80 PA: 43 |
Szabó et al., 2014 [23] | HSI | MIVIS | PBIA | SVM | OA: 79.9 |
Cilia et al., 2015 [13] | HSI | AISA Eagle II | PBIA | SAM | UA: 86 PA: 89 |
Hamedianfar et al., 2014 [24] | HSI | AISA Classic AISA Eagle | OBIA | DT | OA: 93 |
De Pinho et al., 2012 [26] | MSI | IKONOS II | OBIA | DT | OA: 71.91 |
Taherzade et al., 2013 [27] | MSI | WV-2 | OBIA | DT | OA: 82 |
Hamedianfar et al., 2014 [29] | MSI | WV-2 | OBIA | Rule based | OA: 87 |
Hamedianfar et al., 2014 [30] | Fusion of MSI and LiDAR | WV-2 | OBIA | Rule based | OA: 92.84 |
Hamedianfar et al., 2015 [28] | MSI | WV-2 | OBIA | Rule based | OA: 88 |
Gibril et al., 2017 [10] | MSI | WV-2 | OBIA | Rule based | OA: 93 |
Abriha et al., 2018 [31] | MSI | WV-2 | PBIA | RF | OA: 85 |
Tommasini et al., 2019 [11] | MSI | WV-3 | PBIA | RF | OA: 82 |
Norman et al., [32] | Fusion of MSI and LiDAR | WV-3 | OBIA | DT | OA: 87 |
Krowczynska et al., 2020 [12] | MSI | Aerial imagery | DL | CNN | OA: 89 PA: 89 UA: 88 |
Item | Challenge | Area | Effect | Opportunity |
---|---|---|---|---|
1 | The low spatial resolution of early HSI classified by traditional PBIA [13,20,21,22,23] | HSI | Omission Error [13,20] | DL-based semantic classification (e.g., 3D-CNN and FCN) [141,142,145] |
2 | High dimensionality [23,24] | HSI | Curse of dimensionality [34] Inter-class confusions [35] Noisy map [36] | Dimensionality reduction techniques (e.g., SFS and PCA) and OBIA classification [23,152,153] |
3 | Complex patterns of surface materials in VHR MSI and traditional PBIA [10,11,26,27,28,29,30,31,32] | MSI | Within-class spectral variability Between-class spectral similarity Salt-and-pepper error [37,154] | The fusion of MSI with LiDAR and OBIA classification [30,32] |
4 | Non-optimised segmentation parameters (over- or under-segmentation) [28,30] | OBIA | Low classification accuracy, mixed objects [155] | Taguchi optimisation technique [32,47,130,155], and DL-based image segmentation methods such as Mask R-CNN [45], Fusion of segmentation with classification [41,42,107,115] |
5 | Inadequate training data (size and representativeness) [111,112] | Supervised | Low classification accuracy, limitation in transferability and autonomous ACM roof classification areas [117,118] | Adopting a training samples size 30 times greater than the number of RSI spectral bands [118] using advanced non-parametric classifiers (e.g., RF and SVM) [46] Using expert systems [10,32] |
6 | Analyst’s subjectivity [101,105,153,156] | Expert systems | Biased or error-prone results [101,105,153,156] | Adopting automatic induction (i.e., data mining) methods [24,65,120] DL-based end-to-end semantic classification [49] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Abbasi, M.; Mostafa, S.; Vieira, A.S.; Patorniti, N.; Stewart, R.A. Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review. Sustainability 2022, 14, 8068. https://doi.org/10.3390/su14138068
Abbasi M, Mostafa S, Vieira AS, Patorniti N, Stewart RA. Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review. Sustainability. 2022; 14(13):8068. https://doi.org/10.3390/su14138068
Chicago/Turabian StyleAbbasi, Mohammad, Sherif Mostafa, Abel Silva Vieira, Nicholas Patorniti, and Rodney A. Stewart. 2022. "Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review" Sustainability 14, no. 13: 8068. https://doi.org/10.3390/su14138068