WTL-I: Mutual Information-Based Wavelet Transform Learning for Hyperspectral Imaging

Abstract

Hyperspectral imaging (HSI) is useful in many applications, including healthcare, geosciences, and remote surveillance. In general, the HSI data set is large. The use of compressive sensing can reduce these data considerably, provided there is a robust methodology to reconstruct the full image data with quality. This article proposes a method, namely, WTL-I, that is mutual information-based wavelet transform learning for the reconstruction of compressively sensed three-dimensional (3D) hyperspectral image data. Here, wavelet transform is learned from the compressively sensed HSI data in 3D by exploiting mutual information across spectral bands and spatial information within the spectral bands. This learned wavelet basis is subsequently used as the sparsifying basis for the recovery of full HSI data. Elaborate experiments have been conducted on three benchmark HSI data sets. In addition to evaluating the quantitative and qualitative results on the reconstructed HSI data, performance of the proposed method has also been validated in the application of HSI data classification using a deep learning classifier.