A texture feature extraction method considering spatial continuity and gray diversity

• Published in: International journal of applied earth observation and geoinformation 2024-06, Vol.130, p.103896, Article 103896
• Main Authors: Wei, Haishuo, Jia, Kun, Wang, Qiao, Ji, Fengcheng, Cao, Biao, Qi, Jianbo, Zhao, Wenzhi, Yan, Kai, Wang, Guoqiang, Xue, Baolin, Yan, Xing
• Format: Article
• Language: English
• Subjects: Fragmentation degrees; Gray diversity; methodology; remote sensing; Separability; Spatial continuity; spatial data; spatial variation; texture; Texture feature; wills
• ISSN: 1569-8432
• DOI: 10.1016/j.jag.2024.103896

•Proposed a new texture feature extraction method.•This method comprehensively considers spatial continuity and gray diversity.•It could effectively distinguish ground objects with different fragmentation degrees.•It could contribute to achieving the fine recognition of ground objects.•Its performance advantages are more obvious with the decrease of spatial resolution. Texture features play an important role in the field of remote sensing classification. However, most existing methods lack a comprehensive consideration of spatial continuity, which makes them either destroy the spatial integrity of regular ground objects or fail to quantify the fragmentation degrees of irregular ground objects. These problems weak the ability of existing methods to distinguish ground objects with different fragmentation degrees. Therefore, this study proposed a new texture feature extraction method considering spatial continuity and gray diversity (SCGD). SCGD first connected all pixels in a neighborhood in series from end to end according to the row and column directions, and the diversities of the spatial continuity encoding in different directions were calculated by the Shannon index. Then, the Shannon index was used to calculate the gray diversity. Finally, SCGD calculated the weighted average of spatial continuity diversity and gray diversity to obtain the final texture feature values. Validation results indicated that SCGD can effectively distinguish ground objects with different fragmentation degrees, and its performance is better than that of traditional methods. As the spatial resolution decreases, its performance advantage becomes more obvious. Moreover, SCGD has great application potential in the field of ground object classification, and combining it with deep learning models will contribute to achieving the fine recognition of ground objects.