Hyperspectral Band Selection Using Improved Classification Map

Cao, Xianghai and Wei, Cuicui and Han, Jungong and Jiao, Licheng (2017) Hyperspectral Band Selection Using Improved Classification Map. IEEE Geoscience and Remote Sensing Letters, 14 (11). pp. 2147-2151. ISSN 1545-598X

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Abstract

Although it is a powerful feature selection algorithm, the 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 semisupervised 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 data sets. Compared with other wrapper methods, the proposed method shows consistently better performance.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Geoscience and Remote Sensing Letters
Additional Information:
©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
87895
Deposited By:
Deposited On:
06 Oct 2017 19:34
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Nov 2020 04:54