Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery
Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery
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DOI：
10.1080/01431160701469016
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发表时间：
2008-06
影响因子：
3.4
通讯作者：
W. Su;Jing Li;Yunhao Chen;Zhigang Liu;Jinshui Zhang;Tsuey Miin Low;Inbaraj Suppiah;S. Hashim-S.-Has
中科院分区：
文献类型：
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作者：
W. Su;Jing Li;Yunhao Chen;Zhigang Liu;Jinshui Zhang;Tsuey Miin Low;Inbaraj Suppiah;S. Hashim-S.-Has
纹理和局部空间统计信息在使用非常高分辨率的图像对城市区域进行分类时非常重要。本文描述了纹理和局部空间统计在改进QuickBird图像面向对象分类中的作用。在有监督的面向对象分类中，所有的纹理/空间波段都被用作附加波段。纹理分析基于两个层次：分割图像对象和在整个图像上移动窗口。在对图像对象的纹理分析中，45°角二阶矩纹理特征对建筑物的分类效果更好，比其他方向更好地刻画了建筑物的模式。对不同窗口大小(3×3~13×13)的图像进行了基于移动窗口的纹理分析，计算了4个灰度共生矩阵(GLCM)纹理特征(均匀度、对比度、角二阶矩和熵)。7×7窗口大小的对比度特征使分类效果提高了6%。一种局部空间统计，Moran的I特征与垂直邻域规则，进一步提高了分类精度，最高可达7%。光谱和光谱+纹理/空间信息的结果比较表明，纹理和空间信息可以用来改进使用非常高分辨率图像的城市区域面向对象的分类。
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.