A rotation invariant SuperGlue matching algorithm for cross modality image also see here, https://gitee.com/ssacn/RISG-image-matching

• 多时相谷歌地球影像，Optical-optical

• 近红外与光学图像 near-infrared - optical images

• SAR和光学图像，sar-optical

• 光学图像和夜光图像，optical- night light

• 地图与光学图像，map - optical

• 光学图像与激光雷达深度图，optical -lidar depth

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

• PyTorch-GPU 1.10.0+
• OpenCV
• SciPy
• Matplotlib
• pyymal
• pickle

We collected a set of test 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 "test" in this repository.

risgmatching.py contains the majority of the code. Run test_risg.py for testing:

python3 test_risg.py

    with open('./config.yaml', 'r') as f:
        config = yaml.safe_load(f)
        risg = RISGMatcher(config)

        img_filename0 = 'test/01/pair1.jpg'
        img_filename1 = 'test/01/pair2.jpg'

        img0 = cv2.imread(img_filename0)
        img1 = cv2.imread(img_filename1)
        # rotate is number of directions
        mkpts0, mkpts1, conf, main_dir = risg.match(img0,img1,nrotate = 5)