24 September 2013 An efficient classification by signal subspace projection and partial filtering for hyperspectral images
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Abstract
In this study, we propose an efficient classification which combines signal subspace projection (SSP) and partial filtering technique for hyperspectral images. To reduce the computation complexity in image classification, we exploit high degree correlations in spectral and spatial domains. During training process, image bands are first partitioned into several groups for each desired class by Maximum Correlation Band Clustering (MCBC) approach. Then, we design partial filters for each band group by SSP approach. Finally, the SSP-based partial filtering (SSPPF) are combined using corresponding weights for each class. For real image classification, simulations validate the proposed SSPPF can achieve the performance of SSP with less computation complexity. Generally, the proposed method requires only 1/ k2 computations of SSP, if image is partitioned into k groups.
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Lena Chang, Zay-Shing Tang, Hsien-Sen Hung, Yang-Lang Chang, "An efficient classification by signal subspace projection and partial filtering for hyperspectral images", Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 887108 (24 September 2013); doi: 10.1117/12.2023808; https://doi.org/10.1117/12.2023808
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