We develop a deep learning-based matching method between an RGB (red, green and blue) and an infrared image that were captured from satellite sensors. The method includes a convolutional neural network (CNN) that compares the RGB and infrared image pair and a template searching strategy that searches the correspondent point within a search window in the target image to a given point in the reference image. A densely-connected CNN is developed to extract common features from different spectral bands. The network consists of a series of densely-connected convolutions to make full use of low-level features and an augmented cross entropy loss to avoid model overfitting. The network takes band-wise concatenated RGB and infrared images as the input and outputs a similarity score of the RGB and infrared image pair. For a given reference point, the similarity scores within the search window are calculated pixel-by-pixel, and the pixel with the highest score becomes the matching candidate. Experiments on a satellite RGB and infrared image dataset demonstrated that our method obtained more than 75% improvement on matching rate (the ratio of the successfully matched points to all the reference points) over conventional methods such as SURF, RIFT, and PSO-SIFT, and more than 10% improvement compared to other most recent CNN-based structures. Our experiments also demonstrated high performance and generalization ability of our method applying to multitemporal remote sensing images and close-range images.
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