Official implement of Paper:A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

Overview

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images

深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测

Official implement of the Paper:A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. If you find this work helps in your research, please consider citing:

论文《A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images》的官方模型代码。如果该代码对你的研究有所帮助,烦请引用:

Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., & Liu, G. (2020). A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 183-200.

Introduction

This repository includes DSIFN implementations in PyTorch and Keras version and datasets in the paper

该库包含了DSIFN网络的pytorch和keras版本的代码实现以及论文中使用的数据

Model Structure

The overview of Deeply supervised image fusion network (DSIFN). The network has two sub-networks: DFEN with pre-trained VGG16 as the backbone for deep feature extraction and DDN with deep feature fusion modules and deep supervision branches for change map reconstruction.

深度监督影像融合网络框架。该网络包含两个子网络:DFEN(深度特征提取网络)以VGG16为网络基底实现深度特征提取;DDN(差异判别网络)由深度特征融合模块和深度监督分支搭建实现影像变化图重建。

1

Pytorch version requirements

  • Python3.7
  • PyTorch 1.6.0
  • torchversion 0.7.0

Keras version requirements

  • Python 3.6
  • Tensorflow-gpu 1.13.1
  • Keras 2.2.4

Reference

Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., & Liu, G. (2020). A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 183-200.

License

Code and datasets are released for non-commercial and research purposes only. For commercial purposes, please contact the authors.

Owner
Chenxiao Zhang
Postdoc at Wuhan University. Focus on leveraging AI techniques with GIS and RS for smart geospatial observation and analysis.
Chenxiao Zhang
Unofficial PyTorch implementation of Fastformer based on paper "Fastformer: Additive Attention Can Be All You Need"."

Fastformer-PyTorch Unofficial PyTorch implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Usage : import t

Hong-Jia Chen 126 Dec 06, 2022
This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

9 Sep 01, 2022
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective

FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective Official implementation of "FL-WBC: Enhan

Jingwei Sun 26 Nov 28, 2022
Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a

19 Dec 10, 2022
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty

czczup 148 Dec 27, 2022
A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

DGC-Net: Dense Geometric Correspondence Network This is a PyTorch implementation of our work "DGC-Net: Dense Geometric Correspondence Network" TL;DR A

191 Dec 16, 2022
A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.

Built with ❤️ by Sam Showalter Contents Overview Installation Dependencies Usage Scripts Standard Execution Environment Development Environment Benchm

SRI-AIC 1 Nov 18, 2021
The implementation of "Bootstrapping Semantic Segmentation with Regional Contrast".

ReCo - Regional Contrast This repository contains the source code of ReCo and baselines from the paper, Bootstrapping Semantic Segmentation with Regio

Shikun Liu 128 Dec 30, 2022
Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"

Contrastive Self-Supervised Learning on CIFAR-10 Paper "Towards the Generalization of Contrastive Self-Supervised Learning", Weiran Huang, Mingyang Yi

Weiran Huang 13 Nov 30, 2022
Transformer in Vision

Transformer-in-Vision Recent Transformer-based CV and related works. Welcome to comment/contribute! Keep updated. Resource SCENIC: A JAX Library for C

Yong-Lu Li 1.1k Dec 30, 2022
Algorithmic trading using machine learning.

Algorithmic Trading This machine learning algorithm was built using Python 3 and scikit-learn with a Decision Tree Classifier. The program gathers sto

Sourav Biswas 101 Nov 10, 2022
Classification Modeling: Probability of Default

Credit Risk Modeling in Python Introduction: If you've ever applied for a credit card or loan, you know that financial firms process your information

Aktham Momani 2 Nov 07, 2022
PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick."

PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick." [Project page] [Paper

Gyungin Shin 59 Sep 25, 2022
Chunkmogrify: Real image inversion via Segments

Chunkmogrify: Real image inversion via Segments Teaser video with live editing sessions can be found here This code demonstrates the ideas discussed i

David Futschik 112 Jan 04, 2023
Remote sensing change detection using PaddlePaddle

Change Detection Laboratory Developing and benchmarking deep learning-based remo

Lin Manhui 15 Sep 23, 2022
(CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"

(CVPR 2022) TokenCut Pytorch implementation of Tokencut: Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut Yangtao W

YANGTAO WANG 200 Jan 02, 2023
A PyTorch Implementation of ViT (Vision Transformer)

ViT - Vision Transformer This is an implementation of ViT - Vision Transformer by Google Research Team through the paper "An Image is Worth 16x16 Word

Quan Nguyen 7 May 11, 2022
Final report with code for KAIST Course KSE 801.

Orthogonal collocation is a method for the numerical solution of partial differential equations

Chuanbo HUA 4 Apr 06, 2022
A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

24 Dec 13, 2022