NOTE: The CODE is UNDER maintenance since 13 Oct 2020. Codes and modifications will continue to be updated.
Results for Paper: Superpixel-enhanced Deep Neural Forest for Remote Sensing Image Semantic Segmentation
- Python 3.6.2
- Tensorflow 1.6.0
- Numpy 1.13.1
- Opencv-python
- Matplotlib
- Scipy
- Download the test image (RGB for Potsdam/IRRG for Vaihingen) and RGB label image (Fully Reference/No Boundary) from ISPRS 2D semantic labelling website.
- Transfer the RGB label image to the corresponding label image (provided).
| Index | R | G | B | |
|---|---|---|---|---|
| Imp | 0 | 255 | 255 | 255 |
| Build | 1 | 0 | 0 | 255 |
| Low | 2 | 0 | 255 | 255 |
| Tree | 3 | 0 | 255 | 0 |
| Car | 4 | 255 | 255 | 0 |
| Cluster | 5 | 255 | 0 | 0 |
| Un | 6 | 0 | 0 | 0 |
- Rename the testing image and label image.
- Download the Pre-trained resnet_v2_101.ckpt and put it into /Code/net/resnet.
- Download the Pre-trained model for Vaihingen Dataset and put it into /Models/Vaihingen.
- Download the Pre-trained model for Potsdam Dataset and put it into /Models/Potsdam.
import predict_potsdam
predict_potsdam.process()
import predict_vaihingen
predict_vaihingen.process()- The results include the predict RGB image, the predict Label image and the results.txt for accuracy.
- The whole evaluation process is about 20min.
| Imp.S. | Imp.S. | Build. | Build. | Low.V. | Low.V. | Tree | Tree | Car | Car | Mean | Mean | OA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F1 | IoU | F1 | IoU | F1 | IoU | F1 | IoU | F1 | IoU | F1 | IoU | ||
| Potsdam | 93.5 | 87.7 | 96.3 | 93.0 | 89.8 | 81.5 | 92.7 | 86.4 | 96.7 | 93.6 | 93.8 | 88.4 | 92.1 |
| Vaihingen | 93.6 | 87.9 | 96.2 | 92.6 | 88.0 | 78.6 | 92.6 | 86.3 | 85.3 | 74.4 | 91.1 | 83.9 | 92.6 |
Our code is developed based on：
ssn_superpixels
pytorch_ssn
fully-differentiable-deep-ndf-tf
Neural-Decision-Forests
tensorflow-deeplab-v3
deeplabv3-Tensorflow
@article{Li2020Superpixel,
title={Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation},
author={Li Mi and Zhenzhong Chen},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={159},
pages={140-152},
year={2020},
}