Authors: Su, Wei¹; Li, Jing²; Chen, Yunhao²; Liu, Zhigang³; Zhang, Jinshui²; Low, Tsuey Miin⁴; Suppiah, Inbaraj⁴; Hashim, Siti Atikah Mohamed⁵

Source: International Journal of Remote Sensing, Volume 29, Number 11, 2008 , pp. 3105-3117(13)

Publisher: Taylor and Francis Ltd

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.

Document Type: Research article

DOI: http://dx.doi.org/10.1080/01431160701469016

Affiliations: 1: State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resource Science and Technology, Beijing Normal University, Beijing 100875, China,College of Information and Electrical Engineering, China Agricultural University, B 2: State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resource Science and Technology, Beijing Normal University, Beijing 100875, China 3: State Key Laboratory of Remote Sensing Science, Beijing Normal University and Institute of Remote Sensing Applications, CAS, Beijing 100875, China 4: Mindmatics Sdn Bhd, 1st Floor Resource C, 57000, Kuala Lumpur, Malaysia 5: Malaysia Centre for Remote Sensing, 50480, Kuala Lumpur, Malaysia

Publication date: 2008-06-01

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