Hyperspectral Subspace Identification Using SURE

TL;DR

This paper introduces a novel automatic method based on Stein's unbiased risk estimator (SURE) for identifying the low-dimensional signal subspace in hyperspectral data, improving accuracy and efficiency.

Contribution

It proposes a new SURE-based approach for hyperspectral subspace identification that is simple, automatic, and performs well against existing methods.

Findings

The SURE-based method accurately estimates the signal subspace.

Experimental results show competitive performance with state-of-the-art techniques.

The method works effectively on both simulated and real hyperspectral data.

Abstract

Remote sensing hyperspectral sensors collect large volumes of high dimensional spectral and spatial data. However, due to spectral and spatial redundancy the true hyperspectral signal lies on a subspace of much lower dimension than the original data. The identification of the signal subspace is a very important first step for most hyperspectral algorithms. In this paper we investigate the important problem of identifying the hyperspectral signal subspace by minimizing the mean squared error (MSE) between the true signal and an estimate of the signal. Since the MSE is uncomputable in practice, due to its dependency on the true signal, we propose a method based on the Stein's unbiased risk estimator (SURE) that provides an unbiased estimate of the MSE. The resulting method is simple and fully automatic and we evaluate it using both simulated and real hyperspectral data sets. Experimental results shows that our proposed method compares well to recent state-of-the-art subspace identification methods.