Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China
Abstract
1. Introduction
2. Study Area
3. Materials and Methods
3.1. Data Acquisition
3.1.1. Visual Interpretation of Gullies
3.1.2. Imagery Preprocessing
3.1.3. Grey Level Cooccurrence Matrix Feature
3.1.4. Topographic–Hydrologic Features
3.2. Feature Analysis
3.3. Automatic Gully Extraction
3.3.1. Training
3.3.2. Testing
3.3.3. Validating
4. Results
4.1. Gully Distribution
4.2. Feature Analysis
4.2.1. RGB and NIR Bands
4.2.2. GLCM Features
- (1)
- Blue band
- (2)
- Green band
- (3)
- Red band
- (4)
- NIR band
4.2.3. Topographic–Hydrologic Features
4.2.4. Variable Selection
4.3. Model Performance
4.3.1. Sample Statistics
4.3.2. Performance Assessment
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Hussain, M.A.; Li, L.; Kalu, A.; Wu, X.; Naumovski, N. Sustainable food security and nutritional challenges. Sustainability 2025, 17, 874. [Google Scholar] [CrossRef]
- Papargyropoulou, E.; Ingram, J.; Poppy, G.M.; Quested, T.; Valente, C.; Jackson, L.A.; Hogg, T.; Achterbosch, T.; Sicuro, E.P.; Bryngelsson, S.; et al. Research framework for food security and sustainability. Npj Sci. Food 2025, 9, 13. [Google Scholar] [CrossRef] [PubMed]
- Montgomery, D.R. Soil security and global food security. Front. Agric. Sci. Eng. 2024, 11, 297–302. [Google Scholar] [CrossRef]
- Liu, Y.; Wang, C.; Wang, E.; Mao, X.; Liu, Y.; Hu, Z. Raster scale farmland productivity assessment with multi-source data fusion—A case of typical black soil region in Northeast China. Remote Sens. 2024, 16, 1435. [Google Scholar] [CrossRef]
- Shen, H.; Hu, W.; Che, X.; Li, C.; Liang, Y.; Wei, X. Assessment of effectiveness and suitability of soil and water conservation measures on hillslopes of the black soil region in Northeast China. Agronomy 2024, 14, 1755. [Google Scholar] [CrossRef]
- Li, Y.; Chen, Z.; Chen, Y.; Li, T.; Wang, C.; Li, C. Predicting the spatial distribution of soil organic carbon in the black soil area of Northeast Plain, China. Sustainability 2025, 17, 396. [Google Scholar] [CrossRef]
- Han, X.; Zou, W. Research perspectives and footprint of utilization and protection of black soil in Northeast China. Acta Pedol. Sin. 2021, 58, 1341–1358. [Google Scholar] [CrossRef]
- Kong, D.; Chu, N.; Luo, C.; Liu, H. Analyzing spatial distribution and influencing factors of soil organic matter in cultivated land of Northeast China, implications for black soil protection. Land 2024, 13, 1028. [Google Scholar] [CrossRef]
- Kang, L.; Wu, K. Impact of spatial evolution of cropland pattern on cropland suitability in black soil region of Northeast China, 1990–2020. Agronomy 2025, 15, 172. [Google Scholar] [CrossRef]
- Wu, Z.; Jiang, J.; Dong, W.; Cui, S. The spatiotemporal characteristics and driving factors of soil degradation in the black soil region of Northeast China. Agronomy 2024, 14, 2870. [Google Scholar] [CrossRef]
- Wang, H.; Yang, S.; Wang, Y.; Gu, Z.; Xiong, S.; Huang, X.; Sun, M.; Zhang, S.; Guo, L.; Cui, J.; et al. Rates and causes of black soil erosion in Northeast China. Catena 2022, 214, 106250. [Google Scholar] [CrossRef]
- Wang, T. Research on Characteristics and Influencing Factors of Soil Erosion in Bin County. Master’s Thesis, Northeast Agricultural University, Harbin, China, 2021. (In Chinese with English Abstract). [Google Scholar] [CrossRef]
- Liu, J.; Zhu, Y.; Li, J.; Kong, X.; Zhang, Q.; Wang, X.; Peng, D.; Zhang, X. Short-term artificial revegetation with herbaceous species can prevent soil degradation in a black soil erosion gully of Northeast China. Land 2024, 13, 1486. [Google Scholar] [CrossRef]
- Zhang, J.; Wang, M.; Liu, K.; Zhao, Z. Dynamic changes in soil erosion and challenges to grain productivity in the black soil region of Northeast China. Ecol. Indic. 2025, 171, 113145. [Google Scholar] [CrossRef]
- Vrieling, A.; Jong, S.M.D.; Sterk, G.; Rodrigues, S.C. Timing of erosion and satellite data, a multi-resolution approach to soil erosion risk mapping. Int. J. Appl. Earth Obs. Geoinf. 2008, 10, 267–281. [Google Scholar] [CrossRef]
- Wang, R.; Zhang, S.; Pu, L.; Yang, J.; Yang, C.; Chen, J.; Guan, C.; Wang, Q.; Chen, D.; Fu, B. Gully erosion mapping and monitoring at multiple scales based on multi-source remote sensing data of the Sancha River Catchment, Northeast China. ISPRS J. Int. Geo-Inf. 2016, 5, 200. [Google Scholar] [CrossRef]
- Yan, T.; Zhao, W.; Xu, F.; Shi, S.; Qin, W.; Zhang, G.; Fang, N. Is it reliable to extract gully morphology parameters based on high-resolution stereo images? a case of gully in a “soil-rock dual structure area”. Remote Sens. 2024, 16, 3500. [Google Scholar] [CrossRef]
- Zhang, C.; Wang, C.; Long, Y.; Pang, G.; Shen, H.; Wang, L.; Yang, Q. Comparative analysis of gully morphology extraction suitability using unmanned aerial vehicle and Google Earth imagery. Remote Sens. 2023, 15, 4302. [Google Scholar] [CrossRef]
- Zhang, J.; Zhang, Y.; Li, K.; Chen, C.; Liang, Y.; Yang, R. Accessing accuracy of extracting gully and ephemeral gully in the Songnen typical black soil region based on GF-7 satellite images. Sci. Soil Water Conserv. 2024, 22, 152–161, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
- Chen, J. Study on Erosion Gully Extraction Technology Based on GF-7 Satellite. Master’s Thesis, Beijing Forestry University, Beijing, China, 2022. (In Chinese with English Abstract). [Google Scholar] [CrossRef]
- Bokaei, M.; Samadi, M.; Hadavand, A.; Moslem, A.P.; Soufi, M.; Bameri, A.; Sarvarinezhad, A. Gully extraction and mapping in Kajoo-Gargaroo watershed-comparative evaluation of DEM-based and image-based machine learning algorithm. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, X-4-W1-2022, 101–108. [Google Scholar] [CrossRef]
- Shelhamer, E.; Long, J.; Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 640–651. [Google Scholar] [CrossRef]
- Wang, Z.; Fan, B.; Tu, Z.; Li, H.; Chen, D. Cloud and snow identification based on DeepLabV3+and CRF combined model for GF-1WFV images. Remote Sens. 2022, 14, 4880. [Google Scholar] [CrossRef]
- Phinzi, K.; Abriha, D.; Bertalan, L.; Holb, I.; Szabó, S. Machine learning for gully feature extraction based on a pan-sharpened multispectral image, multiclass vs. binary approach. ISPRS Int. J. Geo-Inf. 2020, 9, 252. [Google Scholar] [CrossRef]
- Wang, B. Extraction and Risk Assessment of Erosion Gully in Black Soil Area of Northeast China. Ph.D. Thesis, University of Chinese Academy of Sciences, Beijing, China, 2022. (In Chinese with English Abstract). [Google Scholar] [CrossRef]
- Feng, Q.; Jiang, Z.; Niu, B.; Gao, B.; Yang, J.; Yang, K. Multiscale feature extraction model for remote sensing identification of erosion gullies in Northeast China’s black soil region, a case study of Hailun City. Natl. Remote Sens. Bull. 2024, 28, 3147–3157, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
- Chen, C.; Zhang, Y.; Li, K.; Yang, R.; Zhang, J.; Liang, Y. A method of automatic mapping of gullies based on GF-7 satellite image in the black soil region in Northeast China. Bull. Surv. Mapp. 2024, 1–7, (In Chinese with English abstract). [Google Scholar] [CrossRef]
- Shen, Y.; Su, N.; Zhao, C.; Yan, Y.; Feng, S.; Liu, Y.; Xiang, W. A foreground-driven fusion network for gully erosion extraction utilizing UAV orthoimages and digital surface models. IEEE Trans. Geosci. Remote Sens. 2024, 62, 4409116. [Google Scholar] [CrossRef]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84–90. [Google Scholar] [CrossRef]
- Chen, Y.; Jiang, H.; Li, C.; Jia, X.; Ghamisi, P. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 2016, 54, 6232–6251. [Google Scholar] [CrossRef]
- Tu, F.; Yin, S.; Ouyang, P.; Tang, S.; Liu, L.; Wei, S. Deep convolutional neural network architecture with reconfigurable computation patterns. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 2017, 25, 2220–2233. [Google Scholar] [CrossRef]
- Krichen, M. Convolutional neural networks. Computers 2023, 12, 151. [Google Scholar] [CrossRef]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016. [Google Scholar] [CrossRef]
- Gao, S.; Liang, H.; Hu, D.; Hu, X.; Lin, E.; Huang, H. SAM-ResNet50, a deep learning model for the identification and classification of drought stress in the seedling stage of betula luminifera. Remote Sens. 2024, 16, 4141. [Google Scholar] [CrossRef]
- Stateczny, A.; Uday Kiran, G.; Bindu, G.; Ravi Chythanya, K.; Ayyappa Swamy, K. Spiral search grasshopper features selection with VGG19-ResNet50 for remote sensing object detection. Remote Sens. 2022, 14, 5398. [Google Scholar] [CrossRef]
- Ravula, A.K.; Kovvur, R.M.R. Optimizing deep residual networks, incorporating separable convolutions into ResNet50 architecture. Int. J. Commun. Netw. Inf. Secur. 2024, 16, 108–116. [Google Scholar]
- Ma, J.; Shi, D.; Tang, X.; Zhang, X.; Jiao, L. Dual modality collaborative learning for cross-source remote sensing retrieval. Remote Sens. 2022, 14, 1319. [Google Scholar] [CrossRef]
- Anwer, R.M.; Khan, F.S.; Van De Weijer, J.; Molinier, M.; Laaksonen, J. Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS J. Photogramm. Remote Sens. 2018, 138, 74–85. [Google Scholar] [CrossRef]
- Zhang, X.; Wang, G.; Zhu, P.; Zhang, T.; Li, C.; Jiao, L. GRS-Det, an anchor-free rotation ship detector based on Gaussian-mask in remote sensing images. IEEE Trans. Geosci. Remote Sens. 2020, 59, 3518–3531. [Google Scholar] [CrossRef]
- Chen, L.; Zhu, Y.; Papandreou, G.; Schroff, F.; Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the Computer Vision-ECCV 2018, Munich, Germany, 8–14 September 2018. [Google Scholar] [CrossRef]
- Wang, C.; Zhang, R.; Chang, L. A study on the dynamic effects and ecological stress of eco-environment in the headwaters of the Yangtze River based on improved DeepLabV3+ Network. Remote Sens. 2022, 14, 2225. [Google Scholar] [CrossRef]
- Zheng, K.; Wang, H.; Qin, F.; Miao, C.; Han, Z. An improved land use classification method based on DeepLabV3+ under GauGAN data enhancement. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 16, 5526–5537. [Google Scholar] [CrossRef]
- Wieland, M.; Martinis, S.; Kiefl, R.; Gstaiger, V. Semantic segmentation of water bodies in very high-resolution satellite and aerial images. Remote Sens. Environ. 2023, 287, 113452. [Google Scholar] [CrossRef]
- Sun, Y.; Hao, Z.; Guo, Z.; Liu, Z.; Huang, J. Detection and mapping of Chestnut using deep learning from high-resolution UAV-based RGB imagery. Remote Sens. 2023, 15, 4923. [Google Scholar] [CrossRef]
- Sussi; Husni, E.; Yusuf, R.; Harto, A.B.; Suwardhi, D.; Siburian, A. Utilization of improved annotations from object-based image analysis as training data for DeepLabV3+ model, a focus on road extraction in very high-resolution orthophotos. IEEE Access 2024, 12, 67910–67923. [Google Scholar] [CrossRef]
- Lan, Z.; Liu, Y. Study on multi-scale window determination for GLCM texture description in high-resolution remote sensing image geo-analysis supported by GIS and domain knowledge. ISPRS Int. J. Geo-Inf. 2018, 7, 175. [Google Scholar] [CrossRef]
- Iqbal, N.; Mumtaz, R.; Shafi, U.; Zaidi, S.M.H. Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms. PeerJ Comput. Sci. 2021, 19, e536. [Google Scholar] [CrossRef] [PubMed]
- Hu, Y.; Qi, Z.; Zhou, Z.; Qin, Y. Detection of benggang in remote sensing imagery through integration of segmentation anything model with object-based classification. Remote Sens. 2024, 16, 428. [Google Scholar] [CrossRef]
- Ciccolini, U.; Bufalini, M.; Materazzi, M.; Dramis, F. Gully erosion development in drainage basins, a new morphometric approach. Land 2024, 13, 792. [Google Scholar] [CrossRef]
- Liu, C.; Fan, H. Research advances and prospects on gully erosion susceptibility assessment based on statistical modeling. Trans. Chin. Soc. Agric. Eng. 2024, 40, 29–40, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
- Guan, Q.; Tong, Z.; Arabameri, A.; Santosh, M.; Mondal, I. Scrutinizing gully erosion hotspots to predict gully erosion susceptibility using ensemble learning framework. Adv. Space Res. 2024, 74, 2941–2957. [Google Scholar] [CrossRef]
- Wilkinson, S.N.; Rutherfurd, I.D.; Brooks, A.P.; Bartley, R. Achieving change through gully erosion research. Earth Surf. Process Landf. 2024, 49, 49–57. [Google Scholar] [CrossRef]
- Liu, P.; Wang, C.; Ye, M.; Han, R. Coastal zone classification based on U-net and remote sensing. Appl. Sci. 2024, 14, 7050. [Google Scholar] [CrossRef]
- Chen, Z.; Liu, T.; Yang, K.; Li, Y. Spatial temporal patterns of ecological-environmental attributes within different geological topographical zones, a case from Hailun District, Heilongjiang Province, China. Front. Environ. Sci. 2024, 12, 1393031. [Google Scholar] [CrossRef]
- Zheng, H.; Du, P.; Chen, J.; Xia, J.; Li, E.; Xu, Z.; Li, X.; Yokoya, N. Performance evaluation of downscaling sentinel-2 imagery for land use and land cover classification by spectral-spatial features. Remote Sens. 2017, 9, 1274. [Google Scholar] [CrossRef]
- Yang, M.; Hu, Y.; Tian, H.; Khan, F.A.; Liu, Q.; Goes, J.I.; Gomes, H.d.R.; Kim, W. Atmospheric correction of airborne hyperspectral CASI data using Polymer, 6S and FLAASH. Remote Sens. 2021, 13, 5062. [Google Scholar] [CrossRef]
- Zubair, A.R.; Alo, O.A. Grey level co-occurrence matrix (GLCM) based second order statistics for image texture analysis. Int. J. Sci. Eng. Investig. 2019, 8, 64–73. [Google Scholar] [CrossRef]
- Shi, C.; Zhang, Q.; Liu, Z.; Sun, F. A micro-motion jamming classification and recognition method based on grey-level co-occurrence matrix. J. Air Force Eng. Univ. 2022, 23, 35–42. Available online: http://kjgcdx.ijournal.cn/kjgcdxxb/article/pdf/20220406 (accessed on 17 July 2025). (In Chinese with English Abstract).
- Wang, D.; Wu, Y.; Wei, J.; Zhao, X.; Zhang, H.; Zhu, C.; Yuan, A. Fracture dynamic evolution features of a coal-containing gas based on gray level co-occurrence matrix and industrial CT scanning. Chin. J. Eng. 2023, 45, 31–43, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
- Rumman, A.H.; Barua, K.; Monju, S.I.; Hasan Abed, M.R.; Tan-Ema, S.J.; Sharab, J.F.; Ahmed, S. Classification of CoCr-based magnetic thin films via GLCM texture features extracted from EFTEM images and machine learning. AIP Adv. 2024, 14, 115017. [Google Scholar] [CrossRef]
- Hu, J.; Shen, L.; Albanie, S.; Sun, G.; Wu, E. Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 2020, 42, 2011–2023. [Google Scholar] [CrossRef] [PubMed]
- Rezaei-Dastjerdehei, M.; Mijani, A.; Fatemizadeh, E. Addressing imbalance in multi-label classification using weighted cross entropy loss function. In Proceedings of the 27th National and 5th International Iranian Conference on Biomedical Engineering, Tehran, Iran, 26–27 November 2020; pp. 333–338. [Google Scholar] [CrossRef]
- Yao, C.; Lv, D.; Li, H.; Fu, J.; Li, C.; Gao, X.; Hong, D. A real-time crop lodging recognition method for combine harvesters based on machine vision and modified DeepLabV3+. Smart Agric. Technol. 2025, 11, 100926. [Google Scholar] [CrossRef]
- Markoulidakis, I.; Rallis, I.; Georgoulas, I.; Kopsiaftis, G.; Doulamis, A.; Doulamis, N. Multiclass confusion matrix reduction method and its Application on net promoter score classification problem. Technologies 2021, 9, 81. [Google Scholar] [CrossRef]
- Chunhabundit, P.; Arayapisit, T.; Srimaneekarn, N. Sex prediction from human tooth dimension by ROC curve analysis, a preliminary study. Sci. Rep. 2025, 15, 6627. [Google Scholar] [CrossRef]
- De Raadt, A.; Warrens, M.J.; Bosker, R.J.; Kiers, H.A.L. Kappa coefficients for missing data. Educ. Psychol. Meas. 2019, 79, 558–576. [Google Scholar] [CrossRef]
- Singh, R.; Biswas, M.; Pal, M. Cloud detection using sentinel 2 imageries: A comparison of XGBoost, RF, SVM, and CNN algorithms. Geocarto Int. 2022, 38, 1–32. [Google Scholar] [CrossRef]
- Wang, Z.; Zhang, G.; Wang, C.; Xing, S. Gully morphological characteristics and topographic threshold determined by UAV in a small watershed on the Loess Plateau. Remote Sens. 2022, 14, 3529. [Google Scholar] [CrossRef]
- Na, J.; Yang, X.; Tang, G.; Dang, W.; Strobl, J. Population characteristics of loess gully system in the Loess Plateau of China. Remote Sens. 2020, 12, 2639. [Google Scholar] [CrossRef]
- Zhao, C.; Shen, Y.; Su, N.; Yan, Y.; Feng, S.; Xiang, W.; Liu, Y.; Zhao, T. A label correction learning framework for gully erosion extraction using high-resolution remote sensing images and noisy labels. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024, 17, 1638–1655. [Google Scholar] [CrossRef]
Original Data | Attributes | Correspondingly Derived Data (Quantity) |
---|---|---|
GaoFen-2 Level-1A | Sensor type: two panchromatic and multi-spectral sensors (PMS1 and PMS2); Panchromatic band (1 m): 450~900 nm; Multi spectral bands (4 m): 450~520 nm, 520~590 nm, 630~690 nm, 770~890 nm; Revisit time: 5 (swing) or 69 days; Acquisition date: 30 October 2023; Number of imageries: 4 | Ground true gully; Reflectance bands (4); GLCM mean, variance, homogeneity, contrast, dissimilarity, entropy, ASM, and correlation of 4 reflectance bands (32) |
DEM | Spatial resolution: 2 m | Elevation difference, slope, water accumulation, distance to waterflow (4) |
Level | Concept | Imagery |
---|---|---|
1 | Without clear boundary, can be crossed by agricultural machinery, can be removed by ploughing | |
2 | With a width of 0.3~0.5 m, can be crossed by agricultural machinery, cannot be removed by ploughing | |
3 | With a width greater than 0.5 m, V-shaped cross-section, slope-similar lengthwise section, no fluvial sediment present | |
4 | With a width greater than 0.5 m, V-shaped cross-section, concave-down lengthwise section, a few fluvial sediments present | |
5 | With a width greater than 0.5 m, U-shaped cross-section, flat lengthwise section, mass fluvial sediment present | |
6 | With a width greater than 0.5 m, U-shaped cross-section, flat lengthwise section, mass fluvial sediment present, with steady surface waterflow, some extension with meander |
Automatic Extraction | Gully | Background | Producer’s Accuracy | |
---|---|---|---|---|
Ground Truth | ||||
Gully | pixel quantity of true positive (TP) | pixel quantity of false negative (FN) | ||
Background | pixel quantity of false positive (FP) | pixel quantity of true negative (TN) | ||
User’s accuracy | ||||
, | ||||
, |
Band Name | Main Range | Peak Value | Shape | Sub-Peak | IOUFDC | |
---|---|---|---|---|---|---|
Blue | Gully | 0.05~0.20 | 0.12 | normal | no | 0.2214 |
Surroundings | 0.05~0.25 | 0.17 | normal | no | ||
Green | Gully | 0.09~0.21 | 0.14 | normal | no | 0.1937 |
Surroundings | 0.10~0.26 | 0.20, 0.10 | approximately normal | yes | ||
Red | Gully | 0.10~0.26 | 0.15 | normal | no | 0.1997 |
Surroundings | 0.10~0.30 | 0.23, 0.17 | approximately normal | yes | ||
NIR | Gully | 0.10~0.36 | 0.20 | normal | no | 0.2366 |
Surroundings | 0.10~0.40 | 0.31, 0.22 | approximately normal | yes |
Feature Name | Main Range | Peak Value | Shape | Sub-Peak | IOUFDC | |
---|---|---|---|---|---|---|
Mean | Gully | 4~11 | 7 | normal with aiguille | no | 0.2016 |
Surroundings | 5~12 | 10, 8 | irregular | yes | ||
Variance | Gully | 0~4.5 | 0.2 | normal with aiguille | no | 0.4192 |
Surroundings | 0~4 | 0.2, 0 | irregular | yes | ||
Homogeneity | Gully | 0.4~1 | 0.8 | normal | no | 0.4107 |
Surroundings | 0.4~1 | 0.9 | approximately normal | no | ||
Contrast | Gully | 0~4 | 0.5 | normal with aiguille | no | 0.4951 |
Surroundings | 0~4 | 0 | Single-slope-inclined | no | ||
Dissimilarity | Gully | 0~1 | 0.35 | normal | no | 0.4133 |
Surroundings | 0~1 | 0.2, 0 | irregular | yes | ||
Entropy | Gully | 0~3 | 1.75 | normal | no | 0.4068 |
Surroundings | 0~3 | 1.2, 0 | irregular | yes | ||
ASM | Gully | 0~1 | 0.15 | normal | no | 0.4246 |
Surroundings | 0~1 | 0.33 | irregular | no | ||
Correlation | Gully | −0.2~1 | 0.65 | normal | no | 0.6615 |
Surroundings | −0.2~1 | 0.55 | approximately normal | no |
Feature Name | Main Range | Peak Value | Shape | Sub-Peak | IOUFDC | |
---|---|---|---|---|---|---|
Mean | Gully | 3~13 | 8 | normal with aiguille | no | 0.1842 |
Surroundings | 3~15 | 13 | irregular | no | ||
Variance | Gully | 0~4 | 0.5 | normal with aiguille | no | 0.4955 |
Surroundings | 0~4 | 0 | single slope inclined | no | ||
Homogeneity | Gully | 0.4~1 | 0.84 | normal | no | 0.4276 |
Surroundings | 0.4~1 | 0.92 | approximately normal | no | ||
Contrast | Gully | 0~2 | 0.3 | normal | no | 0.4220 |
Surroundings | 0~2 | 0, 0.2 | irregular | yes | ||
Dissimilarity | Gully | 0~1.5 | 0.3 | normal | no | 0.4476 |
Surroundings | 0~1.5 | 0, 0.2 | irregular | yes | ||
Entropy | Gully | 0~3 | 1.7 | normal | no | 0.4139 |
Surroundings | 0~3 | 0, 1.1 | irregular | yes | ||
ASM | Gully | 0.05~1 | 0.17 | normal | no | 0.4547 |
Surroundings | 0.05~1 | 0.35 | irregular | no | ||
Correlation | Gully | −0.2~1 | 0.65 | normal | no | 0.7012 |
Surroundings | −0.2~1 | 0.63 | approximately normal | no |
Feature Name | Main Range | Peak Value | Shape | Sub-Peak | IOUFDC | |
---|---|---|---|---|---|---|
Mean | Gully | 7~15 | 9, 7.6 | normal with aiguille | yes | 0.1958 |
Surroundings | 7~17 | 15, 14, 16 | irregular | yes | ||
Variance | Gully | 0~2 | 0.25 | normal with aiguille | no | 0.5048 |
Surroundings | 0~2 | 0.25, 0 | irregular | yes | ||
Homogeneity | Gully | 0.4~1 | 0.84 | normal | no | 0.5168 |
Surroundings | 0.4~1 | 0.92 | approximately normal | no | ||
Contrast | Gully | 0~1.8 | 0.3 | normal | no | 0.5088 |
Surroundings | 0~1.8 | 0.2 | approximately normal | no | ||
Dissimilarity | Gully | 0~1.2 | 0.3 | approximately normal | yes | 0.5124 |
Surroundings | 0~1.2 | 0.2 | approximately normal | yes | ||
Entropy | Gully | 0~2.8 | 1.6 | normal | no | 0.4303 |
Surroundings | 0~3 | 0, 1 | irregular | yes | ||
ASM | Gully | 0.05~1 | 0.18 | normal | no | 0.5205 |
Surroundings | 0.05~1 | 0.38, 0.2 | irregular | yes | ||
Correlation | Gully | −0.2~1 | 0.65 | normal | no | 0.7403 |
Surroundings | −0.2~1 | 0.65, 0.85 | approximately normal | yes |
Feature Name | Main Range | Peak Value | Shape | Sub-Peak | IOUFDC | |
---|---|---|---|---|---|---|
Mean | Gully | 7~20 | 12 | normal with aiguille | no | 0.2394 |
Surroundings | 7~25 | 18.5, 20 | approximately normal | yes | ||
Variance | Gully | 0~4 | 0.25 | normal with aiguille | no | 0.4607 |
Surroundings | 0~4 | 0.25, 0 | irregular | yes | ||
Homogeneity | Gully | 0.4~1 | 0.82 | normal | no | 0.4670 |
Surroundings | 0.4~1 | 0.92 | approximately normal | no | ||
Contrast | Gully | 0~2.5 | 0.3 | normal | no | 0.4754 |
Surroundings | 0~2.5 | 0.2, 0 | approximately normal | yes | ||
Dissimilarity | Gully | 0~1.5 | 0.3 | normal | no | 0.4599 |
Surroundings | 0~1.5 | 0.2 | approximately normal | no | ||
Entropy | Gully | 0~3 | 1.75 | normal | no | 0.4243 |
Surroundings | 0~3 | 1, 0 | irregular | yes | ||
ASM | Gully | 0.05~1 | 0.18 | normal | no | 0.4830 |
Surroundings | 0.05~1 | 0.38, 0.18 | irregular | yes | ||
Correlation | Gully | −0.2~1 | 0.8 | normal | no | 0.6712 |
Surroundings | −0.2~1 | 0.68, 0.75 | approximately normal | yes |
Feature Name | Main Range | Peak Value | Shape | Sub-Peak | IOUFDC | |
---|---|---|---|---|---|---|
Elevation difference | Gully | 0~6 m | 2 m | normal with aiguille | no | 0.2912 |
Surroundings | 0~4 m | 0.7 m | normal | no | ||
Slope | Gully | 0~35° | 3° | normal | no | 0.3261 |
Surroundings | 0~12° | 1° | normal | no | ||
Water accumulation | Gully | 0~120,000 m2 | 0 m2 | single-slope-inclined | no | 0.9120 |
Surroundings | 0~1000 m2 | 0 m2 | single-slope-inclined | no | ||
Distance to waterflow | Gully | 0~50 m | 0 m | single-slope-inclined | no | 0.1998 |
Surroundings | 0~160 m | - | single-slope-inclined | no |
Practice NO. | Selected Variables |
---|---|
Practice #1 | Red, green, blue, and NIR bands (baseline, 4 variables in total). |
Practice #2 | Red, green, blue, and NIR bands; Elevation difference, slope, and distance to waterflow (7 variables in total). |
Practice #3 | Red, green, blue, and NIR bands; GLCM variance of blue band (5 variables in total). |
Practice #4 | Red, green, blue, and NIR bands; GLCM variance of blue band; Elevation difference, slope, and distance to waterflow (8 variables in total). |
Performance/Practice NO. | Practice #1 (RGB, NIR) | Practice #2 (RGB, NIR + Topo-Hydro) | Practice #3 (RGB, NIR + GLCM) | Practice #4 (RGB, NIR + GLCM +Topo-Hydro) | |
---|---|---|---|---|---|
Trainset | IOUGully | 0.5188 ± 0.0172 | 0.5479 ± 0.0135 | 0.5062 ± 0.0228 | 0.5461 ± 0.0125 |
Test set | IOUGully | 0.4249 ± 0.0148 | 0.4400 ± 0.0133 | 0.3714 ± 0.0089 | 0.4372 ± 0.0099 |
Validation area | AUC | 0.9585 ± 0.0035 | 0.9661 ± 0.0074 | 0.9420 ± 0.0041 | 0.9702 ± 0.0032 |
Optimal threshold | 0.4060 ± 0.1301 | 0.4960 ± 0.3259 | 0.6920 ± 0.2406 | 0.4400 ± 0.2393 | |
Gully user accuracy | 0.5644 ± 0.0122 | 0.6133 ± 0.0206 | 0.4967 ± 0.0304 | 0.5880 ± 0.0100 | |
Gully producer accuracy | 0.6434 ± 0.0077 | 0.6876 ± 0.0060 | 0.6063 ± 0.0259 | 0.7147 ± 0.0276 | |
Kappa | 0.5753 ± 0.0087 | 0.6256 ± 0.0145 | 0.5151 ± 0.0259 | 0.6213 ± 0.0157 | |
IOUGully | 0.4299 ± 0.0082 | 0.4796 ± 0.0146 | 0.3755 ± 0.0229 | 0.4762 ± 0.0163 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 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
Chen, Z.; Liu, T. Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China. Remote Sens. 2025, 17, 2563. https://doi.org/10.3390/rs17152563
Chen Z, Liu T. Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China. Remote Sensing. 2025; 17(15):2563. https://doi.org/10.3390/rs17152563
Chicago/Turabian StyleChen, Zhuo, and Tao Liu. 2025. "Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China" Remote Sensing 17, no. 15: 2563. https://doi.org/10.3390/rs17152563
APA StyleChen, Z., & Liu, T. (2025). Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China. Remote Sensing, 17(15), 2563. https://doi.org/10.3390/rs17152563