Representative Band Selection for Hyperspectral Image Classification
AbstractThe high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while attempting to remove the redundancy between bands and maintain a good classification ability. In this study, a novel hybrid filter-wrapper band selection method is proposed by a three-step strategy, i.e., band subset decomposition, band selection and band optimization. Based on the information gain (IG) and the spectral curve of the hyperspectral dataset, the band subset decomposition technique is improved, and a random selection strategy is suggested. The implementation of the first two steps addresses the problem of reducing inter-band redundancy. An optimization strategy based on a gray wolf optimizer (GWO) ensures that the selected band combination has a good classification ability. The classification performance of the selected band combination is verified on the Indian Pines, Pavia University and Salinas hyperspectral datasets with the aid of support vector machine (SVM) with a five-fold cross-validation. By comparing the proposed IG-GWO method with five state-of-the-art band selection approaches, the superiority of the proposed method for HSIs classification is experimentally demonstrated on three well-known hyperspectral datasets. View Full-Text
Share & Cite This Article
Xie, F.; Li, F.; Lei, C.; Ke, L. Representative Band Selection for Hyperspectral Image Classification. ISPRS Int. J. Geo-Inf. 2018, 7, 338.
Xie F, Li F, Lei C, Ke L. Representative Band Selection for Hyperspectral Image Classification. ISPRS International Journal of Geo-Information. 2018; 7(9):338.Chicago/Turabian Style
Xie, Fuding; Li, Fangfei; Lei, Cunkuan; Ke, Lina. 2018. "Representative Band Selection for Hyperspectral Image Classification." ISPRS Int. J. Geo-Inf. 7, no. 9: 338.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.