Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62450
Title: A new methodology for spectral-spatial classification of hyperspectral images
Authors: Miao, Z 
Shi, WZ 
Issue Date: 2016
Publisher: Hindawi Publishing Corporation
Source: Journal of sensors, 2016, 1538973 How to cite?
Journal: Journal of sensors 
Abstract: Recent developments in hyperspectral images have heightened the need for advanced classification methods. To reach this goal, this paper proposed an improved spectral-spatial method for hyperspectral image classification. The proposed method mainly consists of three steps. First, four band selection strategies are proposed to utilize the statistical region merging (SRM) method to segment the hyperspectral image. The segmentation map is subsequently integrated with the pixel-wise classification method to classify the hyperspectral image. Finally, the final classification result is obtained using the decision fusion rule. Validation tests are performed to evaluate the performance of the proposed approach, and the results indicate that the new proposed approach outperforms the state-of-the-art methods.
URI: http://hdl.handle.net/10397/62450
ISSN: 1687-725X
EISSN: 1687-7268
DOI: 10.1155/2016/1538973
Rights: Copyright © 2016 Zelang Miao and Wenzhong Shi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following article: Miao, Z., & Shi, W. (2016). A new methodology for spectral-spatial classification of hyperspectral images. Journal of Sensors, 2016, is available at https//doi.org/10.1155/2016/1538973
Appears in Collections:Journal/Magazine Article

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