Mutual information based multi-modal remote sensing image registration using adaptive feature weight

Multi-module images registration is a challenging task in image processing, and more especially in the field of remote sensing. In this letter, we strive to present a novel mutual information scheme for image registration in remote sensing scenario based on feature map technique. We firstly take saliency detection advantages to extract geographic pattern, and then utilize the efficient Laplacian of Gaussian(LOG) and Guided Filter methods to construct a new feature map based on different characteristic of multi-channel images. To avoid practical traps of sub-optimization, we propose an novel mutual information(MI) algorithm based on an adapted weight strategy. The proposed model divides an image into patches and assigns weighted values according to patch similarities in order to solve the optimization problem, improve accuracy and enhance performance. Note that, our proposed method incorporates the LOG and Guided Filter methods into image registration for the first time to construct a new feature map based on differences and similarities strategy. Experiments are conducted over island and coastline scenes, and reveal that our hybrid model has a significant performance and outperforms the state-of-the-art methods in remote sensing image registration.