Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network

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In this repository, we design two branches with convolutional layers in different kernel sizes in each layer of the encoder to capture multi-scale features. Besides, a channel attention block and a global pooling module are utilized to enhance channel consistency and global contextual consistency. Substantial experiments are conducted on both 2D RGB images datasets and 3D spatial-temporal datasets.

The detailed results can be seen in the Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network.

The training and testing code can refer to GeoSeg.

The related repositories include:

• MACU-Net->A revised U-Net structure.
• MAResU-Net->Another type of attention mechanism with linear complexity.

If our code is helpful to you, please cite:

Li, R., Zheng, S., Duan, C., Wang, L., & Zhang, C. (2021). Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network. Geo-spatial Information Science.

Acknowlegement:

Thanks to the providers of the following open-source datasets:

WHDLD

GID

2015&2017

Requirements：

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

Network:

Fig. 1. The structure of the proposed Multi-Scale Fully Convolutional Network.

Result:

Fig. 2. Visualization of results on the WHDLD and GID datasets.

Fig. 3. Visualization of results on the 2015 and 2017 datasets.