Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery

Abstract

Textural and local spatial statistical information is important in the classification of urban areas using very high resolution imagery. This paper describes the utility of textural and local spatial statistics for the improvement of object‐oriented classification for QuickBird imagery. All textural/spatial bands were used as additional bands in the supervised object‐oriented classification. The texture analysis is based on two levels: segmented image objects and moving windows across the whole image. In the texture analysis over image objects, the angular second moment textural feature at a 45° angle showed an improved classification performance with regard to buildings, depicting the patterns of buildings better than any other directions. The texture analysis based on moving windows across the whole image was conducted with various window sizes (from 3×3 to 13×13), and four grey‐level co‐occurrence matrix (GLCM) textural features (homogeneity, contrast, angular second moment, and entropy) were calculated. The contrast feature with the 7×7 window size improved classification up to 6%. One type of local spatial statistics, Moran's I feature with the vertical neighbourhood rule, improved the classification accuracy even further, up to 7%. Comparison of results between spectral and spectral+textural/spatial information indicated that textural and spatial information can be used to improve the object‐oriented classification of urban areas using very high resolution imagery.

Acknowledgements

This research was funded by the Natural Science Foundation of China (40671130), the Beijing Natural Science Foundation (4072016), the Malaysian Centre for Remote Sensing, Airborne Remote Sensing (MARS) Programme, Programme for Changjiang Scholars and Innovative Research Team in University (PCSIRT). We are grateful to Yunxiu Guan, Beijing Space Eye Innovation Technology Company, for guidance in texture analysis. We thank the Director of MACRES and MARS project manager for their continuous guidance and support in this research.