MACU-Net for semantic segmentation of fine-resolution remotely sensed images

Li, Rui; Duan, Chenxi; Zheng, Shunyi; Zhang, Ce; Atkinson, Peter M.. 2022 MACU-Net for semantic segmentation of fine-resolution remotely sensed images. IEEE Geoscience and Remote Sensing Letters, 19, 8007205. 5, pp.

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Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder–decoder architecture, has been used frequently for image segmentation with high accuracy. In this letter, we incorporate multiscale features generated by different layers of U-Net and design a multiscale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) the multiscale skip connections combine and realign semantic features contained in both low-level and high-level feature maps; (2) the asymmetric convolution block strengthens the feature representation and feature extraction capability of a standard convolution layer. Experiments conducted on two remotely sensed data sets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-Netpyramid pooling layers (PPL), U-Net 3+, among other benchmark approaches. Code is available at .

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Soils and Land Use (Science Area 2017-)
ISSN: 1545-598X
Additional Keywords: asymmetric convolution block (ACB), fine-resolution remotely sensed images, semantic segmentation
NORA Subject Terms: Earth Sciences
Data and Information
Date made live: 08 Apr 2022 15:25 +0 (UTC)

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