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This paper proposes a band-subset-based clustering and fusion technique to improve the classification performance in hyperspectral imagery. The proposed method can account for the varying data qualities and discrimination capabilities across spectral bands, and utilize the spectral and spatial information simultaneously. First, the hyperspectral data cube is partitioned into several nearly uncorrelated subsets, and an eigenvalue-based approach is proposed to evaluate the confidence of each subset. Then, a nonparametric technique is used to extract the arbitrarily-shaped clusters in spatial-spectral domain. Each cluster offers a reference spectral, based on which a pseudosupervised hyperspectral classification scheme is developed by using evidence theory to fuse the information provided by each subset. The experimental results on real Hyperspectral Digital Imagery Collection Experiment (HYDICE) demonstrate that the proposed pseudosupervised classification scheme can achieve higher accuracy than the spatially constrained fuzzy c-means clustering method. It can achieve nearly the same accuracy as the supervised K-Nearest Neighbor (KNN) classifier but is more robust to noise.