CSE-HRNet: A context and semantic enhanced high-resolution network for semantic segmentation of aerial imagery

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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.

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APA

Wang, F., Piao, S., & Xie, J. (2020). CSE-HRNet: A context and semantic enhanced high-resolution network for semantic segmentation of aerial imagery. IEEE Access, 8, 182475–182489. https://doi.org/10.1109/ACCESS.2020.3028445

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