FSSBP: Fast Spatial–Spectral Back Projection Based on Pan-Sharpening Iterative Optimization

Pan-sharpening is an important means to improve the spatial resolution of multispectral (MS) images. Although a large number pan-sharpening methods have been developed, improving MS while effectively maintaining its spectral information has not well solved so far, and it also taken as criterion measure whether sharpened product can meet practical needs. The back-projection (BP) method iteratively injects backwards into results in post-processing manner, which generally unsatisfied consistency problem methods. BP received some attention recent years research, existing related work basically limited direct utilization process lacks more in-depth intrinsic integration with pan-sharpening. In this paper, we analyze current problems based on pan-sharpening, main innovative works carried out basis include following: (1) We introduce condition propose spatial–spectral (SSBP) method, takes account both conditions, quality solving distortion results. (2) proposed SSBP analyzed theoretically, convergence relaxed for specific type, degradation transpose BP, are given proved theoretically. (3) Fast computation investigated, non-iterative fast (FBP) algorithms (FSSBP) closed-form solution significant improvement computational efficiency. Experimental comparisons combinations formed by seven different BP-related up 18 typical base show that applicable optimization various sharpening improves speed at least 27.5 times compared iterative version evaluation metrics well.