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- W3089769990 abstract "Semantic segmentation of high-resolution aerial images is a concerning issue of remote sensing applications. To address the issues of intra-class heterogeneity and inter-class homogeneity, a novel end-to-end semantic segmentation network, namely Context and Semantic Enhanced High-Resolution Network (CSE-HRNet), is proposed in this paper. Two procedures are considered comprehensively, which are multi-scale contextual feature extractor and multi-level semantic feature producer. Nested Dilated Residual Block (NDRB) is designed firstly, which could enhance the representational power of multi-scale contexts and tackle the issue of intra-class heterogeneity. The pyramidal feature hierarchy is introduced secondly, by which multi-level feature fusions could be utilized to enlarge inter-class semantic differences. Experimental results verify that, based on the Potsdam and Vaihingen benchmarks, the proposed CSE-HRNet can achieve competitive performance compared with other state-of-the-art methods." @default.
- W3089769990 created "2020-10-08" @default.
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- W3089769990 date "2020-01-01" @default.
- W3089769990 modified "2023-10-06" @default.
- W3089769990 title "CSE-HRNet: A Context and Semantic Enhanced High-Resolution Network for Semantic Segmentation of Aerial Imagery" @default.
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- W3089769990 doi "https://doi.org/10.1109/access.2020.3028445" @default.
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