NDLI: Adaptive expansion of training samples for improving hyperspectral image classification performance

Content Provider	IEEE Xplore Digital Library
Author	Imani, M. Ghassemian, H.
Copyright Year	2013
Description	Author affiliation: Fac. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran (Imani, M.; Ghassemian, H.)
Abstract	A relevant problem for supervised classification of hyperspectral image is the limited availability of labeled training samples, since their collection is generally expensive, difficult and time consuming. In this paper, we propose an adaptive method for improving classification of hyperspectral images through expansion of training samples size. The represented approach utilizes high-confidence labeled pixels as training samples to re-estimate classifier parameters. Semi-labeled samples are samples whose class labels are determined by ML classifier. Samples that their discriminator function values are large enough are selected in an adaptive process and considered as semi-labeled (pseudo-training) samples added to the training samples to train the classifier sequentially. The results of experiments show classification performance is improved and this method can solve the limitation of training samples in hyperspectral images.
Starting Page	1
Ending Page	6
File Size	774723
Page Count	6
File Format	PDF
ISBN	9781467356343
DOI	10.1109/IranianCEE.2013.6599564
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-14
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Classification Classification algorithms Pseudo-training samples Reliability Hyperspectral image Limited training data Covariance matrices Hyperspectral imaging
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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