Dynamic Spectral Similarity Method (DSSM)—A Novel Method for Automated Identification of Objects in Hyperspectral Imagery

Abstract

Automatic identification of object of interest in a hyperspectral imagery is promising for remote sensing applications. Spectral knowledge transfer enables autonomous comparison of reference and imagery spectra for expert-independent analysis. Knowledge-transfer-based analysis involves comparing image spectra to the reference spectra (spectral libraries) using spectral similarity metrics. However, the reference spectral databases and the imagery acquired by different sensors differ in spectral resolution and bandwidths, limiting the direct comparison of the spectra. Thus, prerequisite process of spectral resampling is required before the analysis. We propose a new method “dynamic spectral similarity method (DSSM)” that quantitatively compares spectra from sensors having different spectral resolutions. DSSM geometrically aligns two nonlinear spectra and computes an optimal alignment cost through a time-warping process in a dynamic feature space. We demonstrated the potential of DSSM by comparing spectra of diverse landscape elements obtained from various sources (satellites, airborne, spectral libraries) against reference databases. Furthermore, the proposed method is compared with spectral matching methods [spectral angle mapper (SAM), spectral information divergence2 (SID), normalized spectral similarity score (NS3)] after a spectral alignment process using a Gaussian diffusion model. The results are promising, offering 80%–90% matching accuracy in all the scenarios. DSSM enables seamless comparison of images with varying spectral characteristics, allowing selective and automatic object identification.