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Local model-based hyperspectral pansharpening algorithm via optimization constraint equation and sliding window

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Abstract

Injection model-based algorithms have been proved to be effective techniques to solve the pansharpening problem. However, the existing injection model-based algorithms often face an imbalance between over-sharpening and image blurring in the fused image. This paper proposes a local model-based pansharpening method to solve this problem from two aspects. First, an optimization constraint equation formed by the quality index is proposed to reduce the difference between the details of hyperspectral (HS) images and panchromatic (PAN) images. Second, the sliding-window-based fusion scheme is proposed for the first time, to the best of our knowledge, to adaptively fuse the details of HS and PAN images to reduce redundancy. Simulation experiments show that the proposed algorithm has excellent fusion performance from the aspects of subjectivity and objectivity.

© 2019 Optical Society of America

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