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JMAN

This is official Pytorch implementation of "Efficient Pansharpening by Joint-modality Recursive Training"

Framework

Framework

The overall framework of the proposed JMAN.

Introduction

Multispectral images captured by remote sensing systems usually have shallow spatial resolution. Pansharpening offers a promising solution by enhancing the resolution of these Low-resolution Multi-Spectral (LMS) images to a High-resolution Multi-Spectral (HMS) without the need for costly hardware upgrades. Unfortunately, existing pansharpening models achieve impressive results with numerous learnable parameters, which makes them impractical for integration into remote sensing systems. Moreover, the existing methods suffer from utilizing only the CNN model while ignoring the global information of the image. In this work, a parameter-efficient pansharpening model, named Joint-Modality Association Network (JMAN), is built by leveraging complementary information from multiple modalities and recursive training. It has successfully improved the resolution of remote sensing images, enabling them to have broad applications in environmental monitoring and assessment. Specifically, we efficiently leverage the complementary informa- tion from different networks, including the transformer and convolutional neural network (CNN), and use hierarchical asso- ciation mechanisms to create a more distinctive and informative representation by associating intra-modality and cross-modality. Furthermore, the parameter-sharing mechanism of recursive training can effectively reduce the number of parameters in the model. Benefiting from its lightweight design and effective information fusion strategy, the proposed method can generate faithful pansharpened multispectral images that excel in both spectral and spatial resolution. Experimental results show the superiority of the proposed method over extensive benchmarks.

Recommended Environment

  • Python3.7+, Pytorch>=1.6.0
  • NVIDIA GPU + CUDA
  • Run python setup.py develop

Quick Start

Step0. Set your Python environment.

git clone https://github.com/songwenhao123/JMAN(Pytorch)

Then,

python setup.py develop

Step1. Put datasets and set path

  • Put datasets (WorldView-3, QuickBird, GaoFen2, WorldView2) into the UDL/Data/pansharpening, see following path structure.
|-$ROOT/Data
├── pansharpening
│   ├── training_data
│   │   ├── train_wv3.h5
│   │   ├── ...
│   ├── validation_data
│   │   │   ├── valid_wv3.h5
│   │   │   ├── ...
│   ├── test_data
│   │   ├── WV3
│   │   │   ├── NY1_WV3_RR.mat
│   │   │   ├── ...
│   │   │   ├── ...

Step2. To train python run_pansharpening.py

Step3. To test python run_test_pansharpening.py