This repository is the official implementation of the paper "Dynamic Convolution Self-Attention Network for Land Cover Classification in VHR Remote Sensing Images".
The current deep convolutional neural networks for Very-High-Resolution (VHR) remote sensing image land cover classification often suffer from two challenges. First, the feature maps extracted by network encoders based on vanilla convolution usually contain a lot of redundant information, which easily causes misclassification of land cover. Moreover, these encoders usually require a large number of parameters and high computational costs. Second, as remote sensing images are complex and contain many objects with large-scale variances, it is difficult to use the popular feature fusion modules to improve the representation ability of feature maps. To address the above issues, we propose a dynamic convolution self-attention network (DCSA-Net) for VHR remote sensing image land cover classification. The proposed network has two advantages. On the one hand, we design a lightweight dynamic convolution module (LDCM) by using dynamic convo-lution and a self-attention mechanism. This module can extract more useful image features than vanilla convolution, avoiding the negative effect of useless feature maps on land cover classifi-cation. On the other hand, we design a context information aggregation module (CIAM) with a ladder structure to enlarge the receptive field. This module can aggregate multi-scale contexture information from feature maps with different resolutions using the dense connection. Experiments results show that the proposed DCSA-Net is superior to state-of-the-art networks due to higher accuracy of land cover classification, fewer parameters, and lower computational cost.
Our model achieves the following performance on land cover classification:
| Dataset | Imp. Surf. | Building | Low veg. | Tree | Car | Mean F1 | OA | mIoU |
|---|---|---|---|---|---|---|---|---|
| Potsdam | 93.69 | 96.34 | 88.05 | 88.87 | 95.63 | 92.52 | 91.25 | 84.24 |
| Vaihingen | 92.11 | 96.19 | 83.04 | 90.31 | 82.39 | 88.81 | 90.58 | 78.93 |
If you found this code useful, please cite the following paper:
(This paper is currently under review. The full publication information will be added.)
@article{DCSA-Net,
title={Dynamic Convolution Self-Attention Network for Land Cover Classification in VHR Remote Sensing Images},
author={Xuan Wang, Yue Zhang, Tao Lei, Yingbo Wang, Yujie Zhai, and Asoke K. Nandi},
}