Spectral-Spatial Classification Integrating Band Selection for Hyperspectral Imagery With Severe Noise Bands

Spectral-spatial classification for hyperspectral imagery has been receiving much attention, since the detailed spectral and rich spatial information of hyperspectral images can be fully exploited to improve the classification accuracy. However, when the original hyperspectral images have very noisy bands, these bands may have an unfavorable impact on the classification, and are often discarded in advance based on expert knowledge. In this study, a spectral-spatial conditional random field classification algorithm integrating band selection (CRFBS) is developed for hyperspectral imagery with severe noise bands. The proposed algorithm integrates band selection based on the relative utility of the spectral bands for classification. Consequently, negative effects of severe noise bands are eliminated and the need for high-quality image data is substantially reduced. In addition, the CRFBS algorithm makes comprehensive use of both the spectral and the spatial cues to improve the classification performance. The spectral cues are formulated by integrating the support vector machine and random forest algorithms to improve the spectral discriminative ability in the unary potentials, and the spatial information are modeled to consider the interactions between pixels in pairwise potentials. The experiments using different airborne and UAV-borne hyperspectral data verified the effectiveness of the CRFBS method. The CRFBS algorithm can achieve accurate interpretation of the various classification categories and a more than 3% improvement in classification accuracy, compared with the method using the original hyperspectral image with severe noise bands.