DocumentCode :
3608572
Title :
Spectral–spatial hyperspectral image classification with adaptive mean filter and jump regression detection
Author :
Zhenyu Lu ; Jueshan He
Author_Institution :
Coll. of Electron. & Inf. Eng., Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
Volume :
51
Issue :
21
fYear :
2015
Firstpage :
1658
Lastpage :
1660
Abstract :
A new remotely sensed hyperspectral image data classification algorithm which integrates the adaptive mean filter and jump regression in a variational framework is introduced. First, the adaptive mean filter is used to build the posterior probability distributions in each subpixel, and the jump detection method is then used to provide the content-aware information to adjust the smoothing extent of total variation in image discontinuous areas. Experimental results on real hyperspectral datasets show the relatively good performance of the proposed algorithm in terms of the overall accuracy, average accuracy and kappa statistic.
Keywords :
adaptive filters; hyperspectral imaging; image classification; probability; regression analysis; variational techniques; adaptive mean filter; content-aware information; image discontinuous areas; jump regression detection; posterior probability distributions; remotely sensed hyperspectral image data classification algorithm; smoothing extent; spectral-spatial hyperspectral image classification; variational framework;
fLanguage :
English
Journal_Title :
Electronics Letters
Publisher :
iet
ISSN :
0013-5194
Type :
jour
DOI :
10.1049/el.2015.2259
Filename :
7300473
Link To Document :
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