MACU-Net for semantic segmentation of fine-resolution remotely sensed images
Li, Rui; Duan, Chenxi; Zheng, Shunyi; Zhang, Ce 
ORCID: https://orcid.org/0000-0001-5100-3584; 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.
10.1109/LGRS.2021.3052886
Abstract/Summary
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 https://github.com/lironui/MACU-Net .
| Item Type: | Publication - Article |
|---|---|
| Digital Object Identifier (DOI): | 10.1109/LGRS.2021.3052886 |
| 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) |
| URI: | https://nora.nerc.ac.uk/id/eprint/532462 |
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