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Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

Welcome to my HomePage

In this repository, we implement the Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in very fine resolution (VFR) urban scene images .

The detailed results can be seen in the Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images.

The training and testing code can refer to GeoSeg.

The related repositories include:

If our code is helpful to you, please cite:

Wang, L.; Li, R.; Wang, D.; Duan, C.; Wang, T.; Meng, X. Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images. Remote Sensing. 2021, 13, 3065. https://doi.org/10.3390/rs13163065

Requirements:

numpy >= 1.16.5
PyTorch >= 1.3.1
sklearn >= 0.20.4
tqdm >= 4.46.1
imageio >= 2.8.0
timm >= 0.4.5

Network:

network
Fig. 1. The overall architecture of BANet.

Result:

The result on the UAVid dataset can seen from here, where the user name is AlexWang and the results 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
BANet 85.4 78.9 66.6 80.7 62.1 52.8 69.3 21.0 64.6

Result
Fig. 2. The experimental results on the UAVid validation set. The first column illustrates the input RGB images, the second column depicts the ground reference and the third column shows the predictions of our BANet.

Result
Fig. 3. 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 BANet.