Monitoring urban impervious surface area change using China-Brazil Earth Resources Satellites and HJ-1 remote sensing images

2015 ◽  
Vol 9 (1) ◽  
pp. 096094 ◽  
Author(s):  
Peijun Du ◽  
Junshi Xia ◽  
Li Feng
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Impervious Surface ◽  
Remote Sensing Images ◽  
Impervious Surface Area ◽  
Area Change ◽  
Urban Impervious Surface ◽  
Surface Area Change

scholarly journals COMBINING SPECTRAL AND TEXTURE FEATURES USING RANDOM FOREST ALGORITHM: EXTRACTING IMPERVIOUS SURFACE AREA IN WUHAN

2016 ◽  
Vol XLI-B7 ◽  
pp. 351-358
Author(s):  
Zhenfeng Shao ◽  
Yuan Zhang ◽  
Lei Zhang ◽  
Yang Song ◽  
Minjun Peng
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Surface Area ◽  
Random Forests ◽  
Impervious Surface ◽  
Impervious Surfaces ◽  
Remote Sensing Images ◽  
Impervious Surface Area

Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, sub-pixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer’s accuracy of 0.98, a user’s accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97.

scholarly journals COMBINING SPECTRAL AND TEXTURE FEATURES USING RANDOM FOREST ALGORITHM: EXTRACTING IMPERVIOUS SURFACE AREA IN WUHAN

2016 ◽  
Vol XLI-B7 ◽  
pp. 351-358 ◽  
Author(s):  
Zhenfeng Shao ◽  
Yuan Zhang ◽  
Lei Zhang ◽  
Yang Song ◽  
Minjun Peng
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Surface Area ◽  
Random Forests ◽  
Impervious Surface ◽  
Impervious Surfaces ◽  
Remote Sensing Images ◽  
Impervious Surface Area

Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, sub-pixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer’s accuracy of 0.98, a user’s accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97.

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