https://github.com/daifeng2016/Change-Detection-Dataset-for-High-Resolution-Satellite-Imagery

This repository is for releasing a dataset for high-resolution satellite imagery
https://github.com/daifeng2016/Change-Detection-Dataset-for-High-Resolution-Satellite-Imagery

Last synced: about 2 months ago
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This repository is for releasing a dataset for high-resolution satellite imagery

• Host: GitHub
• URL: https://github.com/daifeng2016/Change-Detection-Dataset-for-High-Resolution-Satellite-Imagery
• Owner: daifeng2016
• Created: 2020-06-01T21:46:16.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-08-08T09:15:01.000Z (about 4 years ago)
• Last Synced: 2024-07-23T01:42:43.566Z (about 2 months ago)
• Size: 7.81 KB
• Stars: 30
• Watchers: 4
• Forks: 2
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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• awesome-remote-sensing-change-detection - **Google Dataset**

README

        
# Change-Detection-dataset-for-High-resolution-Satellite-Image

This repository is for releasing a change detection dataset for high-resolution satellite imagery.

## paper

SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images [https://ieeexplore.ieee.org/document/9161009]

## Introduction

Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of

deep learning, most recent developments of CD methods have

introduced the use of deep learning techniques to increase

the accuracy and automation level over traditional methods.

However, when using supervised CD methods, a large amount of

labeled data is needed to train deep convolutional networks with

millions of parameters. These labeled data are difficult to acquire

for CD tasks. To address this limitation, a novel semisupervised

convolutional network for CD (SemiCDNet) is proposed based

on a generative adversarial network (GAN). First, both the

labeled data and unlabeled data are input into the segmentation

network to produce initial predictions and entropy maps. Then,

to exploit the potential of unlabeled data, two discriminators

are adopted to enforce the feature distribution consistency of

segmentation maps and entropy maps between the labeled and

unlabeled data. During the competitive training, the generator is

continuously regularized by utilizing the unlabeled information,

thus improving its generalization capability. The effectiveness

and reliability of our proposed method are verified on two

high-resolution remote sensing data sets. Extensive experimental

results demonstrate the superiority of the proposed method

against other state-of-the-art approaches.

## Dataset

The proposed dataset can be found at [https://pan.baidu.com/s/12Ln-nCb15YNu1T28pzC0rA] password: 8quw

## Citation

Please cite our paper if you find it useful for your research.

```

D. Peng, L. Bruzzone, Y. Zhang, H. Guan, H. Ding and X. Huang, "SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3011913.

```