The substantial scale variation and intra-class diversity within remote sensing imagery pose significant challenges for semantic segmentation, rendering methods developed for natural images inapplicable. These challenges, we introduce a novel semantic segmentation model named D-CANet, which primarily comprises three modules: the Global Class Center Awareness (GCCA), the Local Class Awareness Module (LCAM), and the Global Class Generation Module (GCG). Specifically, the GCCA module is dedicated to modeling the global representation of class context to mitigate the interference from image backgrounds; the LCAM module generates a local class representation, serving as an intermediary perceptual element that facilitates an implicit linkage between pixels and global class representations, minimizing the variance within classes; following the processing by the LCAM module, the GCG module enhances the global class representation. This encoder-decoder structure equipped with GCCA, LCAM, and GCG modules achieves precise segmentation of objects of varying scales within remote sensing imagery through the interactive perception and fusion of global and local features. Experimental assessments conducted on the Potsdam dataset and the Vaihingen dataset illustrate that D-CANet surpasses the current state-of-the-art semantic segmentation techniques in terms of efficacy.
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.
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