Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3052886 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Convolution, Semantics, Remote sensing, Image segmentation, Kernel, Feature extraction, Decoding, Asymmetric convolution block (ACB), fine-resolution remotely sensed images, semantic segmentation | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Li | 1 | 19 | 16.31 |
| Shunyi Zheng | 2 | 0 | 2.03 |
| Chenxi Duan | 3 | 0 | 2.03 |