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Superpixel-enhanced Deep Neural Forest for Remote Sensing Image Semantic Segmentation

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
Framework

Environments

  • Python 3.6.2
  • Tensorflow 1.6.0
  • Numpy 1.13.1
  • Opencv-python
  • Matplotlib
  • Scipy

Data

  • 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.

Pre-trained Model

Evaluation

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.

Results

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

Acknowledgements

Our code is developed based on:

ssn_superpixels
pytorch_ssn
fully-differentiable-deep-ndf-tf
Neural-Decision-Forests
tensorflow-deeplab-v3
deeplabv3-Tensorflow

Cite

@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},
}