This work proposes a simple yet effective method to adapt unsupervised convolutional neural networks (CNNs) from multispectral (MS) to hyperspectral (HS) pansharpening. Thus, it focuses on the fusion of a single high-resolution panchromatic (PAN) band with a low-resolution HS data cube. This is achieved by means of a decorrelation transform, following the principal component analysis (PCA) approach, which enables the compression of a significant portion of the HS image energy into a few bands. Afterward, a suitably adapted pansharpening network designed for four spectral bands is used to super-resolve only the principal components (PCs). Experiments demonstrate high performance in both quantitative and qualitative evaluations, favorably comparing against state-of-the-art methods.

PCA-CNN Hybrid Approach for Hyperspectral Pansharpening

Scarpa, G
2023-01-01

Abstract

This work proposes a simple yet effective method to adapt unsupervised convolutional neural networks (CNNs) from multispectral (MS) to hyperspectral (HS) pansharpening. Thus, it focuses on the fusion of a single high-resolution panchromatic (PAN) band with a low-resolution HS data cube. This is achieved by means of a decorrelation transform, following the principal component analysis (PCA) approach, which enables the compression of a significant portion of the HS image energy into a few bands. Afterward, a suitably adapted pansharpening network designed for four spectral bands is used to super-resolve only the principal components (PCs). Experiments demonstrate high performance in both quantitative and qualitative evaluations, favorably comparing against state-of-the-art methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/127099
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