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.

中文翻译：

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一种考虑空间连续性和灰度多样性的纹理特征提取方法


纹理特征在遥感分类领域发挥着重要作用。然而，大多数现有方法缺乏对空间连续性的综合考虑，这使得它们要么破坏了规则地物的空间完整性，要么无法量化不规则地物的破碎程度。这些问题削弱了现有方法区分不同破碎程度地物的能力。因此，本研究提出了一种考虑空间连续性和灰度多样性的纹理特征提取新方法（SCGD）。 SCGD首先将邻域内的所有像素按照行、列方向从头到尾串联起来，通过香农指数计算不同方向上空间连续性编码的多样性。然后，利用香农指数计算灰色多样性。最后，SCGD计算空间连续性多样性和灰度多样性的加权平均，得到最终的纹理特征值。验证结果表明SCGD能够有效区分不同破碎程度的地物，其性能优于传统方法。随着空间分辨率的降低，其性能优势变得更加明显。此外，SCGD在地物分类领域具有巨大的应用潜力，将其与深度学习模型相结合将有助于实现地物的精细识别。