文章基本信息

标题：Quantitative Textural Parameter Selection for Residential Extraction from High-Resolution Remotely Sensed Imagery
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作者：J. Gu ; J. Chen ; Q.M. Zhou 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B4
页码：1371-1376
出版社：Copernicus Publications
摘要：Residential areas show plenty of texture information on high resolution remotely sensed imagery. Appropriate description about this texture information for discriminating residential class and its background is a key problem for improving the classification results. Method for selecting proper texture parameters is presented in this paper. Based on the analysis of residential texture, grey level co- occurrence matrix (GLCM) and edge density (ED) approaches with candidate nine texture measurements (contrast, homogeneity, dissimilarity entropy, energy, mean, standard deviation, correlation and edge density) is selected as candidate texture measurements. The texture parameters are selected based on separability measured by Jeffries-Matusita distance (JM distance) between residential and its background in corresponding texture space. IKONOS panchromatic imagery has been used as example and the optimal texture parameters were selected by using the proposed method
关键词：Texture; Residential Area; JM-Distance; Window Size; Quantization Level; Displacement; Orientation