Improved Generalized IHS Based on Total Variation for Pansharpening

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 11; p. 2945
• Main Authors: Zhang, Xuefeng, Dai, Xiaobing, Zhang, Xuemin, Hu, Yuchen, Kang, Yingdong, Jin, Guang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2023
• Subjects: Accuracy; Algorithms; Computer vision; Decomposition; Deep learning; Distortion; GIHS; Image quality; Machine learning; Objective function; Optimization; pansharpening; Remote sensing; Sensors; Substitutes; total variation; Visual effects; Wavelet transforms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15112945

Pansharpening refers to the fusion of a panchromatic (PAN) and a multispectral (MS) image aimed at generating a high-quality outcome over the same area. This particular image fusion problem has been widely studied, but until recently, it has been challenging to balance the spatial and spectral fidelity in fused images. The spectral distortion is widespread in the component substitution-based approaches due to the variation in the intensity distribution of spatial components. We lightened the idea using the total variation optimization to improve upon a novel GIHS-TV framework for pansharpening. The framework drew the high spatial fidelity from the GIHS scheme and implemented it with a simpler variational expression. An improved L1-TV constraint to the new spatial–spectral information was introduced to the GIHS-TV framework, along with its fast implementation. The objective function was solved by the Iteratively Reweighted Norm (IRN) method. The experimental results on the “PAirMax” dataset clearly indicated that GIHS-TV could effectively reduce the spectral distortion in the process of component substitution. Our method has achieved excellent results in visual effects and evaluation metrics.