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Special Issue "Object-Based Image Analysis"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 July 2011)

Special Issue Editor

Guest Editor
Dr. Stefan Lang

Centre for Geoinformatics, University of Salzburg, Schillerstr. 30, Building XV, 3rd floor, 5020 Salzburg, Austria
E-Mail
Phone: +43 662 8044-5262
Interests: object-based image analysis (OBIA); geomonitoring and geons; integrated spatial indicators; knowledge representation; policy support; GMES

Special Issue Information

Dear Colleagues,

Object-based image analysis (OBIA) has emerged over the last years from integrating geospatial concepts and advanced image analysis techniques. Spatial properties like size and form, neighborhood and context, scale and hierarchy, are utilized for better exploit imagery and other image-like continuous data. In parallel, advances in sensor technology and new processing methods (e.g. grid computing) has strongly supported the maturing of OBIA making it an established approach for image understanding.

OBIA conceptually utilizes two interrelated methodological pillars for effectively handling the complexity of recent highest resolution imagery. These are: (1) segmentation / regionalization for image object delineation and providing scaled and hierarchical representations; (2) advanced classifiers reflecting the interaction between spectral and spatial properties by either using learning algorithms or making these explicit in transferable rule sets. The term image analysis (instead of image classification) is used to stress that the process of OBIA is iterative and cyclic.

Today, driven by international programmes and initiatives like GEO or GMES, the provision of imagery should no longer a bottle-neck per se (leaving aside specific requirements such as real time provision, particular detail or atmospheric conditions). The bigger challenge, instead, is the information extraction and the provision of added-value products with constant quality and reliability, high transferability and re-usability of algorithm and rule sets, and clear validation concept.

This special issue asks for papers that serve a growing scientific community interested in OBIA. Particular topics include: critical assessments of commercial software applications; new developments with potential of operational use; methodological and conceptual achievements for object-based change detection; and object validation and addressing complex (composite) classes.

Dr. Stefan Lang
Guest Editor

Keywords

  • segmentation
  • advanced classifiers
  • spatio-spectral
  • scaled representation
  • automation

Published Papers (7 papers)

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Research

Open AccessArticle Spectral Difference in the Image Domain for Large Neighborhoods, a GEOBIA Pre-Processing Step for High Resolution Imagery
Remote Sens. 2012, 4(8), 2294-2313; doi:10.3390/rs4082294
Received: 19 June 2012 / Revised: 26 July 2012 / Accepted: 30 July 2012 / Published: 7 August 2012
Cited by 3 | PDF Full-text (2102 KB) | HTML Full-text | XML Full-text
Abstract
Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel objects. This should be done before
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Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel objects. This should be done before applying homogeneity criteria in the aggregation of pixels for the construction of meaningful image objects. The habit or “best practice” to start GEOBIA with pixel aggregation into homogeneous objects should come with the awareness that feature attributes for single pixels are at risk of becoming less accessible for further analysis. Single pixel contrast with image convolution on close neighborhoods is a standard technique, also applied in edge detection. This study elaborates on the analysis of close as well as much larger neighborhoods inside the GEOBIA domain. The applied calculations are limited to the first segmentation step for single pixel objects in order to produce additional feature attributes for objects of interest to be generated in further aggregation processes. The equation presented functions at a level that is considered an intermediary product in the sequential processing of imagery. The procedure requires intensive processor and memory capacity. The resulting feature attributes highlight not only contrasting pixels (edges) but also contrasting areas of local pixel groups. The suggested approach can be extended and becomes useful in classifying artificial areas at national scales using high resolution satellite mosaics. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
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Open AccessArticle A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories
Remote Sens. 2012, 4(5), 1310-1336; doi:10.3390/rs4051310
Received: 29 March 2012 / Revised: 27 April 2012 / Accepted: 28 April 2012 / Published: 7 May 2012
Cited by 33 | PDF Full-text (8645 KB) | HTML Full-text | XML Full-text
Abstract
Geoinformation derived from Earth observation (EO) plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response) a semi-automated object-based approach for landslide detection
[...] Read more.
Geoinformation derived from Earth observation (EO) plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response) a semi-automated object-based approach for landslide detection and classification has been developed. The method was applied to a case study in North-Western Italy using SPOT-5 imagery and a digital elevation model (DEM), including its derivatives slope, aspect, curvature and plan curvature. For the classification in the object-based environment spectral, spatial and morphological properties as well as context information were used. In a first step, landslides were classified on a coarse segmentation level to separate them from other features with similar spectral characteristics. Thereafter, the classification was refined on a finer segmentation level, where two categories of mass movements were differentiated: flow-like landslides and other landslide types. In total, an area of 3.77 km² was detected as landslide-affected area, 1.68 km² were classified as flow-like landslides and 2.09 km² as other landslide types. The outcomes were compared to and validated by pre-existing landslide inventory data (IFFI and PAI) and an interpretation of PSI (Persistent Scatterer Interferometry) measures derived from ERS1/2, ENVISAT ASAR and RADARSAT-1 data. The spatial overlap of the detected landslides and existing landslide inventories revealed 44.8% (IFFI) and 50.4% (PAI), respectively. About 32% of the polygons identified through OBIA are covered by persistent scatterers data. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
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Open AccessArticle Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery
Remote Sens. 2011, 3(12), 2707-2726; doi:10.3390/rs3122707
Received: 19 October 2011 / Revised: 9 December 2011 / Accepted: 9 December 2011 / Published: 16 December 2011
Cited by 11 | PDF Full-text (2249 KB) | HTML Full-text | XML Full-text
Abstract
The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings
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The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
Open AccessArticle An Object-Based Classification of Mangroves Using a Hybrid Decision Tree—Support Vector Machine Approach
Remote Sens. 2011, 3(11), 2440-2460; doi:10.3390/rs3112440
Received: 20 September 2011 / Revised: 8 November 2011 / Accepted: 10 November 2011 / Published: 17 November 2011
Cited by 56 | PDF Full-text (1985 KB) | HTML Full-text | XML Full-text
Abstract
Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional
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Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA), and support vector machine (SVM) classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863) for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
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Open AccessArticle Object-Based Image Analysis of Downed Logs in Disturbed Forested Landscapes Using Lidar
Remote Sens. 2011, 3(11), 2420-2439; doi:10.3390/rs3112420
Received: 20 September 2011 / Revised: 9 November 2011 / Accepted: 10 November 2011 / Published: 16 November 2011
Cited by 19 | PDF Full-text (1984 KB) | HTML Full-text | XML Full-text
Abstract
Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In
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Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging) sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst’s time in the delineation and classification of downed logs in the lidar data. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
Open AccessArticle Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification
Remote Sens. 2011, 3(10), 2263-2282; doi:10.3390/rs3102263
Received: 19 August 2011 / Revised: 14 October 2011 / Accepted: 14 October 2011 / Published: 21 October 2011
Cited by 40 | PDF Full-text (4467 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study is to compare WorldView-2 (WV-2) and QuickBird-2-simulated (QB-2) imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. Four
[...] Read more.
The objective of this study is to compare WorldView-2 (WV-2) and QuickBird-2-simulated (QB-2) imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. Four different feature selection algorithms were used in order to verify to which data set the most relevant object-based features belong to. Object-based classifications were performed with four different supervised algorithms applied to each data set and the obtained accuracies and model performances indexes were compared. Segmentation experiments carried out involving bands exclusively available in the WV-2 sensor generated segments slightly more similar to our reference segments (only about 0.23 discrepancy). Fifty seven percent of the different selected features and 53% of all the 80 selections refer to features that can only be calculated with the additional bands of the WV-2 sensor. On the other hand, 57% of the most relevant features and 63% of the second most relevant features can also be calculated considering only the QB-2 bands. In 10 out of 16 classifications, higher Kappa values were achieved when features related to the additional bands of the WV-2 sensor were also considered. In most cases, classifications carried out with the 8-band-related features generated less complex and more efficient models than those generated only with QB-2 band-related features. Our results lead to the conclusion that spectrally similar classes like ceramic tile roofs and bare soil, as well as asphalt and dark asbestos roofs can be better distinguished when the additional bands of the WV-2 sensor are used throughout the object-based classification process. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
Open AccessArticle Automatic Geographic Object Based Mapping of Streambed and Riparian Zone Extent from LiDAR Data in a Temperate Rural Urban Environment, Australia
Remote Sens. 2011, 3(6), 1139-1156; doi:10.3390/rs3061139
Received: 12 April 2011 / Revised: 5 May 2011 / Accepted: 17 May 2011 / Published: 30 May 2011
Cited by 14 | PDF Full-text (1800 KB) | HTML Full-text | XML Full-text
Abstract
This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction
[...] Read more.
This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual and shape information. This functionality increases the likelihood of developing transferable and automated mapping approaches. LiDAR data covered parts of the Werribee Catchment in Victoria, Australia, which is characterized by urban, agricultural, and forested land cover types. Field data of streamside vegetation structure and physical form properties were used for both calibration of the mapping routines and validation of the mapping results. To improve the transferability of the rule set, the GEOBIA approach was developed for an area representing different riparian zone environments, i.e., urbanized, agricultural and hilly forested areas. Results show that mapping streambed extent (R2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)

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