Hyperspectral Image Spectral–Spatial-Range Gabor Filtering
Lin He, Chenying Liu, Jun Li, Yuanqing Li, Shutao Li, Zhuliang Yu
Abstract
Spectral-spatial Gabor filtering, which is based on 3-D local harmonic analysis, has been a powerful spectral-spatial feature extraction tool for hyperspectral image (HSI) classification. However, existing spectral-spatial Gabor approaches are prone to oversmoothing, neglecting the existences of edges and negatively affecting the classification. In this article, we propose a new HSI Gabor filtering concept, called spectral-spatial-range Gabor filtering, which intends to restrain edge interference from disturbing local spectral-spatial harmonic components. Contributions and novelties of our work can be identified as follows: 1) an HSI filtering framework is created, which can accommodate various Gabor filtering procedures and hence offer the potential to guide the design of new Gabor filters; 2) following such a unified filtering framework and taking into consideration both local spectral-spatial harmonic characteristics and range domain variations, we develop a new concept of spectral-spatial-range Gabor filtering; and 3) utilizing this proposed Gabor prototype and elaborating mathematical derivations, we achieve a novel discriminative spectral-spatial-range Gabor filtering method, which can deal with discriminative local harmonics and edge interference simultaneously along the spectral-spatial-range domain, obtaining highly discriminative Gabor features while yielding linear computational complexity. Our novel method is evaluated on four real HSI data sets and achieves excellent performances.