Research on forest land classification method of remote sensing images based on boundary information

Semantic segmentation of remote sensing image is a key technology in the field of remote sensing image processing, and its segmentation results can be used in land resource management, yield estimation, disaster evaluation and many other aspects. However, the forest land is widely distributed and the tree species are diverse, which brings difficulties to the extraction of forest land. Different from traditional manual investigation, semantic segmentation can quickly extract forest land from remote sensing images. U-Net is a deep codec structure, which has been frequently used for high-precision image segmentation. In this paper, Multi-scale features of different levels of U-Net are used to extract forest land from high-resolution remote sensing images. Multi-scale features can capture features of different scales for fusion, and note the importance of boundary information. A boundary attention module is added to explicitly use boundary information for context aggregation, which makes the extracted boundary effect more remarkable Attention module is designed to enhance the learning of vegetation characteristics, so as to improve the segmentation performance. This study effectively improves the problems of fuzzy boundary of semantic segmentation, large intra-class differences and small inter-class differences, and can quickly extract forest land.