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ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images

Welcome to my HomePage

In this repository, we implement the Attentive Bilateral Contextual Network which contains a spatial path and a contextual path to fully capture the long-range relationships and fine-grained details in fine-resolution remote sensing images.

The detailed results can be seen in the ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images.

The training and testing code can refer to GeoSeg.

The related repositories include:

If our code is helpful to you, please cite:

R. Li, S. Zheng, C. Zhang, C. Duan, L. Wang, and P. M. Atkinson, "ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 181, pp. 84-98, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.isprsjprs.2021.09.005.

Network:

network
Fig. 1. The overall architecture of ABCNet.

Result:

The result on the UAVid dataset can seen from here, where the user name is lironui or here and can be downloaded by this link:

Method building tree clutter road vegetation static car moving car human mIoU
MSD 79.8 74.5 57.0 74.0 55.9 32.1 62.9 19.7 57.0
Fast-SCNN 75.7 71.5 44.2 61.6 43.4 19.5 51.6 0.0 45.9
BiSeNet 85.7 78.3 64.7 61.1 77.3 63.4 48.6 17.5 61.5
SwiftNet 85.3 78.2 64.1 61.5 76.4 62.1 51.1 15.7 61.1
ShelfNet 76.9 73.2 44.1 61.4 43.4 21.0 52.6 3.6 47.0
ABCNet 86.4 79.9 67.4 81.2 63.1 48.4 69.8 13.9 63.8

Result
Fig. 2. The experimental results on the UAVid test set. The first column illustrates the input RGB images, the second column depicts the outputs of MSD and the third column shows the predictions of our ABCNet.