Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery
Abstract
:1. Introduction
- To evaluate various texture feature selection algorithms and classification procedures;
- To provide a full-scale and optimum feature set and classifier for more efficient and accurate urban land use/cover mapping;
- To help users provide the optimum feature set, significantly reducing the time and effort required for feature selection in the classification process.
- Assessed VHR multispectral and panchromatic image data for extracting various urban land use/covers;
- Extracted and collected multiscale textural features from VHR image data;
- Implemented the wrapper-based and filter-based feature selection approaches;
- Evaluated each feature set with classification performance to obtain the most efficient one;
- Demonstrated the generalized characteristics of selected features for the efficient classification of new images.
- Investigated individual features’ role in the classification performance.
2. Proposed Methodology
2.1. Image Data
2.2. Class Separability Analysis
2.3. Feature Extraction
2.4. Feature Selection (FS)
2.4.1. Filter-Based Feature Selection
2.4.2. Wrapper-Based Feature Selection
2.5. Classification Algorithms
3. Results and Discussion
3.1. Multiscale Textural Feature Extraction
3.2. Classification of Extracted Features
3.3. Analysis of Generalization
3.4. Feature Assessment
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Image Information | ||||||
---|---|---|---|---|---|---|
Satellite | Dimension (Pixels) | Location (Based on Figure 2) | Spatial res. for Panchromatic | Spatial res. for Multispectral | Acquisition Date and Time | Center Point Coordinates |
Worldview-2 | 1040 × 695 | Tehran (Iran) (A) | 0.5 m | 2 m | 4 December 2010 7:15 | 35°44′46.85″N 51°15′38.92″E |
GeoEye-1 | 901 × 588 | Hobart (Australia) (E) | 0.5 m | 2 m | 5 March 2013 13:25 | 42°47′79″S 147°14′53.7″E |
QuickBird | 679 × 646 | Denver (USA) (C) | 0.6 m | 2.4 m | 4 July 2005 18:01 | 40°01′20.04″N 105°17′29.5″W |
Pléiades | 1119 × 634 | Melbourne (Australia) (D) | 0.5 m | 2 m | 25 February 2012 14:52 | 37°49′50.33″S 144°57′50.94″E |
WorldView-3 | 1290 × 664 | Rio de Janeiro (Brazil) (B) | 0.31 m | 1.24 m | 2 May 2016 13:12 | 22°57′22.89″S 43°10′42.20″W |
Appendix B
Worldview-2 Classes | Training Samples (Pixel) | Test Samples (Pixel) |
---|---|---|
Bare soil | 1128 | 12,974 |
Lawn | 3431 | 39,459 |
Highway | 5900 | 67,851 |
Parking | 3246 | 37,539 |
Low-rise building | 3241 | 37,271 |
Road | 5966 | 68,604 |
Sports facility | 1648 | 18,949 |
High-rise building | 2673 | 30,737 |
Tree | 3759 | 43,225 |
Sidewalk | 1361 | 15,648 |
Shrub | 4910 | 56,471 |
Total ROIs | 37,263 | 428,728 |
QuickBird Classes | Training Samples (Pixel) | Test Samples (Pixel) |
---|---|---|
Lawn | 3697 | 42,515 |
Highway | 1224 | 14,078 |
Parking | 3208 | 34,826 |
Low-rise building | 1907 | 22,661 |
Road | 2477 | 28,489 |
Commercial building | 2649 | 30,467 |
Tree | 7680 | 88,323 |
Total ROIs | 22,752 | 261,359 |
GeoEye-1 Classes | Training Samples (Pixel) | Test Samples (Pixel) |
---|---|---|
Bare soil | 6166 | 70,912 |
Lawn | 1553 | 17,863 |
Highway | 1671 | 19,219 |
Parking | 1016 | 11,681 |
Low-rise building | 2509 | 28,856 |
Road | 3115 | 35,820 |
Sports facility | 611 | 7023 |
Commercial building | 2590 | 29,788 |
Tree | 3117 | 35,846 |
Total ROIs | 22,348 | 257,008 |
Pléiades Classes | Training Samples (Pixel) | Test Samples (Pixel) |
---|---|---|
Lawn | 1949 | 22,409 |
Highway | 3176 | 36,527 |
Parking | 1623 | 18,670 |
Low-rise building | 11,319 | 130,164 |
Road | 8505 | 97,808 |
Sports facility | 303 | 3480 |
High-rise building | 5108 | 30,737 |
Tree | 3533 | 40,626 |
Railway | 1949 | 22,418 |
Total ROIs | 37,465 | 402,839 |
WorldView-3 Classes | Train Samples (Pixel) | Test Samples (Pixel) |
---|---|---|
Bare soil | 1418 | 16,306 |
Lawn | 511 | 5876 |
Highway | 1860 | 21,390 |
Parking | 1625 | 18,691 |
Low-rise building | 7680 | 88,321 |
Road | 3774 | 43,398 |
High-rise building | 4273 | 49,136 |
Tree | 4660 | 53,585 |
Shrub | 87 | 1000 |
Total ROIs | 25,888 | 297,703 |
Appendix C
Input Features | ||||||
---|---|---|---|---|---|---|
ASM5_3_0 | Cont5_3_0 | Cor5_3_0 | Dis5_3_0 | Ent5_3_0 | Homo5_3_0 | Mean5 |
ASM5_3_45 | Cont5_3_45 | Cor5_3_45 | Dis5_3_45 | Ent5_3_45 | Homo5_3_45 | Mean9 |
ASM5_3_90 | Cont5_3_90 | Cor5_3_90 | Dis5_3_90 | Ent5_3_90 | Homo5_3_90 | Mean17 |
ASM9_7_0 | Cont9_7_0 | Cor9_7_0 | Dis9_7_0 | Ent9_7_0 | Homo9_7_0 | Mean31 |
ASM9_7_45 | Cont9_7_45 | Cor9_7_45 | Dis9_7_45 | Ent9_7_45 | Homo9_7_45 | Mean51 |
ASM9_7_90 | Cont9_7_90 | Cor9_7_90 | Dis9_7_90 | Ent9_7_90 | Homo9_7_90 | Var5 |
ASM17_15_0 | Cont17_15_0 | Cor17_15_0 | Dis17_15_0 | Ent17_15_0 | Homo17_15_0 | Var9 |
ASM17_15_45 | Cont17_15_45 | Cor17_15_45 | Dis17_15_45 | Ent17_15_45 | Homo17_15_45 | Var17 |
ASM17_15_90 | Cont17_15_90 | Cor17_15_90 | Dis17_15_90 | Ent17_15_90 | Homo17_15_90 | Var31 |
ASM31_15_0 | Cont31_15_0 | Cor31_15_0 | Dis31_15_0 | Ent31_15_0 | Homo31_15_0 | Var51 |
ASM31_15_45 | Cont31_15_45 | Cor31_15_45 | Dis31_15_45 | Ent31_15_45 | Homo31_15_45 | Pan |
ASM31_15_90 | Cont31_15_90 | Cor31_15_90 | Dis31_15_90 | Ent31_15_90 | Homo31_15_90 | |
ASM31_30_0 | Cont31_30_0 | Cor31_30_0 | Dis31_30_0 | Ent31_30_0 | Homo31_30_0 | |
ASM31_30_45 | Cont31_30_45 | Cor31_30_45 | Dis31_30_45 | Ent31_30_45 | Homo31_30_45 | |
ASM31_30_90 | Cont31_30_90 | Cor31_30_90 | Dis31_30_90 | Ent31_30_90 | Homo31_30_90 | |
ASM51_15_0 | Cont51_15_0 | Cor51_15_0 | Dis51_15_0 | Ent51_15_0 | Homo51_15_0 | |
ASM51_15_45 | Cont51_15_45 | Cor51_15_45 | Dis51_15_45 | Ent51_15_45 | Homo51_15_45 | |
ASM51_15_90 | Cont51_15_90 | Cor51_15_90 | Dis51_15_90 | Ent51_15_90 | Homo51_15_90 | |
ASM51_30_0 | Cont51_30_0 | Cor51_30_0 | Dis51_30_0 | Ent51_30_0 | Homo51_30_0 | |
ASM51_30_45 | Cont51_30_45 | Cor51_30_45 | Dis51_30_45 | Ent51_30_45 | Homo51_30_45 | |
ASM51_30_90 | Cont51_30_90 | Cor51_30_90 | Dis51_30_90 | Ent51_30_90 | Homo51_30_90 |
Appendix D
GA Parameter | Value |
---|---|
Population size | 60 |
Elite count | 2 |
Fitness function | KNN-based classification accuracy |
Number of generations | 30 |
Mutation probability | 0.1 |
Crossover probability | 0.8 |
Crossover type | Unique |
PSO Parameter | Value |
---|---|
Population size | 60 |
Fitness function | KNN-based classification accuracy |
Maximum iteration | 30 |
C1 | 2 |
C2 | 2 |
Appendix E
Name of Selection Methods | Content of Optimal Features |
---|---|
GA_Relief-F | Mean31, Mean51, Mean9, Cor51_30_0, Cor51_15_90, Cor51_30_90, Cor31_15_45, Cor31_30_90, Dis51_30_45, Dis51_15_90, Cont51_15_90, Ent51_15_0, Ent31_15_45, Homo51_15_90, |
PSO_Relief-F | Mean51, Mean31, Mean5, Mean9, Cor51_15_90, Cor51_30_0, Cor51_15_0, Cor31_15_45, Cor51_30_90, Cor31_15_0, Cont51_15_90, Cont51_15_0, Dis51_15_90, Dis51_30_45 |
GA_NCA | Mean51, Mean31, Mean5, Cor51_30_0, Cor51_15_90, Cor51_15_0, Cor31_15_45, Homo51_15_45, Homo31_15_90, Dis51_30_45, Dis31_15_45, Dis51_15_45, Var31, Ent31_15_45 |
PSO_NCA | Mean51, Mean31, Cor51_15_90, Cor51_15_0, Cor51_30_0, Cor51_30_90, Cor31_15_0, Cor31_15_45, Var31, Dis51_30_45, Cont51_15_0, Cont51_15_45, Homo51_15_90, Pan |
GA_MRMR | Mean9, Mean31, Mean5, Cor51_30_0, Cor51_15_45, Asm9_7_0, Asm51_15_45, Asm17_15_90, Cont31_30_0, Cont9_7_90, Dis51_15_90, Dis51_30_90, Var31, Var9 |
PSO_MRMR | Mean9, Mean5, Mean31, Mean17, Cor31_15_0, Cor51_15_90, Cor5_3_0, Cor31_15_45, Asm9_7_0, Cont51_15_90, Var31, Ent51_30_45, Var31, Pan |
Appendix F
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Tehran | Denver | Hobart | |
---|---|---|---|
GA | 46 | 51 | 53 |
PSO | 74 | 63 | 75 |
Input Dataset and Classifier | Tehran | Hobart | Denver | |||
---|---|---|---|---|---|---|
F1-Measure | Overall Time (min.) | F1-Measure | Overall Time (min.) | F1-Measure | Overall Time (min.) | |
GA_MRMR_ANN | 0.770 | 17.24 | 0.889 | 3.48 | 0.718 | 4.44 |
GA_MRMR_RF | 0.892 | 2.49 | 0.926 | 1.14 | 0.839 | 1.09 |
GA_MRMR_SVM | 0.874 | 30.31 | 0.953 | 6.32 | 0.761 | 11.18 |
GA_NCA_ANN | 0.788 | 29.80 | 0.934 | 13.83 | 0.852 | 16.94 |
GA_NCA_RF | 0.935 | 14.96 | 0.963 | 8.50 | 0.909 | 8.82 |
GA_NCA_SVM | 0.959 | 37.76 | 0.979 | 13.59 | 0.946 | 18.25 |
GA_ReliefF_ANN | 0.830 | 61.10 | 0.940 | 26.26 | 0.820 | 0.00 |
GA_ReliefF_RF | 0.930 | 44.27 | 0.960 | 20.54 | 0.900 | 16.80 |
GA_ReliefF_SVM | 0.934 | 70.71 | 0.968 | 25.41 | 0.920 | 26.60 |
PSO_MRMR_ANN | 0.715 | 15.29 | 0.874 | 7.09 | 0.747 | 9.53 |
PSO_MRMR_RF | 0.862 | 2.55 | 0.911 | 1.15 | 0.819 | 1.14 |
PSO_MRMR_SVM | 0.842 | 33.22 | 0.959 | 6.41 | 0.736 | 12.11 |
PSO_NCA_ANN | 0.810 | 29.73 | 0.939 | 16.86 | 0.861 | 15.45 |
PSO_NCA_RF | 0.941 | 16.98 | 0.964 | 12.28 | 0.921 | 11.47 |
PSO_NCA_SVM | 0.971 | 40.87 | 0.984 | 17.67 | 0.947 | 22.17 |
PSO_ReliefF_ANN | 0.754 | 77.26 | 0.925 | 30.38 | 0.807 | 34.58 |
PSO_ReliefF_RF | 0.931 | 65.03 | 0.957 | 26.12 | 0.900 | 23.24 |
PSO_ReliefF_SVM | 0.962 | 90.25 | 0.981 | 31.67 | 0.935 | 35.00 |
Classifiers | Melbourne | Rio | ||||
---|---|---|---|---|---|---|
F1-Measure | OA% | Time (min.) | F1-Measure | OA% | Time (min.) | |
SVM | 0.96 | 96.29 | 23.36 | 0.94 | 94.31 | 46 |
RF | 0.93 | 94.11 | 1.40 | 0.90 | 92.15 | 1.03 |
ANN | 0.82 | 86.94 | 12.94 | 0.76 | 86.6 | 32.06 |
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Saboori, M.; Homayouni, S.; Shah-Hosseini, R.; Zhang, Y. Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery. Remote Sens. 2022, 14, 2097. https://doi.org/10.3390/rs14092097
Saboori M, Homayouni S, Shah-Hosseini R, Zhang Y. Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery. Remote Sensing. 2022; 14(9):2097. https://doi.org/10.3390/rs14092097
Chicago/Turabian StyleSaboori, Mojtaba, Saeid Homayouni, Reza Shah-Hosseini, and Ying Zhang. 2022. "Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery" Remote Sensing 14, no. 9: 2097. https://doi.org/10.3390/rs14092097