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Which Target to Focus on: Class-Perception for Semantic Segmentation of Remote Sensing | IEEE Journals & Magazine | IEEE Xplore
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Which Target to Focus on: Class-Perception for Semantic Segmentation of Remote Sensing


Abstract:

Deep-learning-based (DL) methods have dominated the task of semantic segmentation of remote sensing images. However, the sizes of different objects vary widely, and there...Show More

Abstract:

Deep-learning-based (DL) methods have dominated the task of semantic segmentation of remote sensing images. However, the sizes of different objects vary widely, and there is a great deal of label noise due to the inevitable shadows. Therefore, there is an urgent need for a method that can precisely handle complex ground data. In this article, we propose an interclass enhanced network (ICEN) for representing features of varying sizes. It comprises two branches: sparse representation network (SPN) and feature extraction network (FEN). Then, a class-perception block (CPB) is inserted between the two branches to instruct the SPN’s low-level semantic features to be merged into the deeper network. Such a block can reduce label noise in remote sensing image segmentation. In addition, the proposed EIRI provides a more precise classification process for target edges containing many misclassified points without requiring excessive computational overhead. The experimental results of our proposed class-perception network (C-PNet) achieve competitive performance on the Vaihingen, Potsdam, LoveDA, and UAVid datasets.
Article Sequence Number: 4404213
Date of Publication: 19 May 2023

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