 This paper proposes two new algorithms for hyperspectral image classification. The first algorithm uses Gabor filtering to extract spatial information from the images, while the second algorithm uses multi-hypothesis prediction to combine spectral and spatial information. Both algorithms were tested on two real hyperspectral data sets and compared with other state-of-the-art methods. The results showed that both algorithms outperformed existing methods in terms of accuracy and robustness. This article was authored by Chen Chen, Wei Li, Hong Jun Su, and others.