A novel approach to combine spatial and spectral information from hyperspectral images

TL;DR

This paper introduces a flexible framework for jointly analyzing spatial and spectral data in hyperspectral images, demonstrated through applications in wood characterization and plant disease detection.

Contribution

It presents a novel generic framework that combines spatial and spectral features from hyperspectral images using different processing methods and fusion techniques.

Findings

Effective in characterizing teak wood in an unsupervised manner.

Successful early detection of apple scab on leaves.

Shows promise for diverse hyperspectral imaging applications.

Abstract

This article proposes a generic framework to process jointly the spatial and spectral information of hyperspectral images. First, sub-images are extracted. Then each of these sub-images follows two parallel workflows, one dedicated to the extraction of spatial features and the other dedicated to the extraction of spectral features. Finally, the extracted features are merged, producing as many scores as sub-images. Two applications are proposed, illustrating different spatial and spectral processing methods. The first one is related to the characterization of a teak wood disk, in an unsupervised way. It implements tensors of structure for the spatial branch, simple averaging for the spectral branch and multi-block principal component analysis for the fusion process. The second application is related to the early detection of apple scab on leaves. It implements co-occurrence matrices for the spatial branch, singular value decomposition for the spectral branch and multiblock partial least squares discriminant analysis for the fusion process. Both applications demonstrate the interest of the proposed method for the extraction of relevant spatial and spectral information and show how promising this new approach is for hyperspectral imaging processing.