Automated Lightweight Descriptor Generation for Hyperspectral Image Analysis

Abstract

Analyzing hyperspectral images poses a non-trivial challenge due to various challenges. To overcome most of these challenges one of the widely employed approach involves utilizing indices, such as the Normalized Difference Vegetation Index (NDVI). Indices provide a powerful means to distill complex spectral information into meaningful metrics, facilitating the interpretation of specific features within the hyperspectral domain. Moreover, the indices are usually easy to compute. However, creating indices for discerning arbitrary data classes within an image proves to be a challenging task. In this paper, we present an algorithm designed to automatically generate lightweight descriptors, suited for discerning between arbitrary classes in hyperspectral images. These lightweight descriptors within the algorithm are characterized by indices derived from selected informative layers. Our proposed algorithm streamlines the descriptor generation process through a multi-step approach. Firstly, it employs Principal Component Analysis (PCA) to transform the hyperspectral image into a three-channel representation. This transformed image serves as input for a Segment Anything Model (SAM). The neural network outputs a labeled map, delineating different classes within the hyperspectral image. Subsequently, our Informative Index Formation algorithm (INDI) utilizes this labeled map to systematically generate a set of lightweight descriptors. Each descriptor within the set is adept at distinguishing a specific class from the remaining classes in the hyperspectral image. The paper demonstrates the practical application of the developed algorithm for hyperspectral image segmentation.