Enhanced recognition using iterative learning fusion in remote sensing images

This article focuses on remote sensing image fusion in order to improve target recognition performance. Current fusion algorithms are mostly designed for specific purpose and have exponential complexity. We propose a fast and robust image fusion algorithm-the iterative learning fusion (ILF) algorithm, to improve the quality of images. This algorithm combines iterative learning in control theory with Multi-scale Geometric Analysis (MGA) image fusion algorithms; also, we apply color transfer to preserve color feature and cooperate it with SVM to improve recognition. By performing iterative learning, fusion parameters will converge to optimal in MGA fusion process. Theoretical analysis and experiments demonstrate improvement of visual and quantitative performance by proposed algorithm.