Comprehensive Analysis and Improvements in Pansharpening Using Deep Learning
This work addresses spectral fidelity issues in remote sensing pansharpening, but it is incremental as it builds on the existing PSGAN framework.
The paper tackled spectral distortions in pansharpening by enhancing the PSGAN framework with novel regularization techniques, resulting in improved spectral fidelity and superior performance on the Worldview-3 dataset across multiple metrics.
Pansharpening is a crucial task in remote sensing, enabling the generation of high-resolution multispectral images by fusing low-resolution multispectral data with high-resolution panchromatic images. This paper provides a comprehensive analysis of traditional and deep learning-based pansharpening methods. While state-of-the-art deep learning methods have significantly improved image quality, issues like spectral distortions persist. To address this, we propose enhancements to the PSGAN framework by introducing novel regularization techniques for the generator loss function. Experimental results on images from the Worldview-3 dataset demonstrate that the proposed modifications improve spectral fidelity and achieve superior performance across multiple quantitative metrics while delivering visually superior results.