Hyperspectral subpixel target detection based on interaction subspace model

Abstract

In this paper, we examine the problem of detecting subpixel targets in hyperspectral images. The so-called subpixel target refers to a target that only occupies a part of a pixel due to the low spatial resolution of hyperspectral sensors. Considering that the subpixel target spectrum is not always reliable (e.g., due to spectral variability), an interaction subspace model is designed to deal with this problem. In this subspace model, the second-order interaction terms are introduced to better describe the spectral variability, thereby improving the robustness. Specifically, the subspace model uses a hyperplane in a high-dimensional space to model spectral variability, while traditional models (e.g., the additive model and the replacement model) use a line in the high-dimensional space to model spectral variability. Obviously, the stronger description ability of the hyperplane makes the subspace model more tolerant to the mismatch of the target spectrum. Based on this interaction subspace model, we derive adaptive detectors according to the one-step generalized likelihood ratio test and its two-step variant. Experiments conducted on hyperspectral data demonstrate that the proposed two-step detector exhibits the strongest robustness in cases where the target spectrum is not very reliable.