Aiming at the problem that the differences in heterogeneous remote sensing images in imaging modes, time phases, and resolutions make matching difficult, a new deep learning feature matching method is proposed. The results show that the algorithm in this paper has strong adaptability and robustness, and is superior to other algorithms in terms of the number and distribution of matching points, efficiency, and adaptability. This repository contains the implementation of the following paper:

"Deep learning algorithm for feature matching of cross modality remote sensing images" （in Chinese）
异源遥感影像特征匹配的深度学习算法

paper pdf

The main idea and code of feature extracting in this repository are based on D2-Net.

Matching result between google earth images （in 2009 & 2018）

Matching result between uav optical image and thermal infrared image

Matching result between SAR image （GF-3) & optical satellite(ZY-3) image

Matching result between satellite image & map

Python 3.7+ is recommended for running our code. Conda can be used to install the required packages:

• PyTorch 1.4.0+
• OpenCV
• SciPy
• Matplotlib
• skimage

We collected a set of test data named "df-sm-data", including images from space-borne SAR and visible light sensors, drone thermal infrared sensors, and Google Earth images. You may find them in the directory "df-sm-data" in this repository.

The off-the-shelf VGG16 weights and their tuned counterpart can be downloaded by running:

mkdir models
wget https://dsmn.ml/files/d2-net/d2_tf.pth -O models/d2_tf.pth

cnnmatching.py contains the majority of the code. Run cnnmatching.py for testing:

python3 cnnmatching.py

You may change the images path in the code just like:

imgfile1 = 'df-ms-data/1/df-googleearth-500-20091227.jpg'
imgfile2 = 'df-ms-data/1/df-googleearth-500-20181029.jpg'