Although it is a powerful feature selection algorithm, wrapper method is rarely used for hyperspectral band selection. Its accuracy is restricted by the number of labeled training samples and collecting such label information for hyperspectral image is time consuming and expensive. Benefited from the local smoothness of hyperspectral images, a simple yet effective semi-supervised wrapper method is proposed, where the edge preserved filtering is exploited to improve the pixel-wised classification map and this in turn can be used to assess the quality of band set. The property of the proposed method lies in using the information of abundant unlabeled samples and valued labeled samples simultaneously. The effectiveness of the proposed method is illustrated with five real hyperspectral datasets. Compared with other wrapper methods, the proposed method shows consistently better performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.