Fusion of circulant singular spectrum analysis and multiscale local ternary patterns for effective spectral-spatial feature extraction and small sample hyperspectral image classification
Scientific Research Fund of Hunan Provincial Education Department; HSI Analysis group; NSF
机构署名：
本校为第一且通讯机构
院系归属：
计算机科学与技术学院
摘要：
Hyperspectral images (HSIs) contain rich spectral and spatial information, motivating the development of a novel circulant singular spectrum analysis (CiSSA) and multiscale local ternary pattern fusion method for joint spectral-spatial feature extraction and classification. Due to the high dimensionality and redundancy in HSIs, principal component analysis (PCA) is used during preprocessing to reduce dimensionality and enhance computational efficiency. CiSSA is then applied to the PCA-reduced images for robust spatial pattern extraction via circulant matrix decomposition. The spatial features ...
