NDLI: A Pixon-based hyperspectral image segmentation method used for remote sensing data classification

Content Provider	IEEE Xplore Digital Library
Author	Zehtabian, A. Ghassemian, H.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran (Zehtabian, A.; Ghassemian, H.)
Abstract	Image segmentation plays a key role in remote sensing especially as a preprocessing step for further applications such as classification. The data dimensionality and high spectral resolution of the images make it more challenging to precisely segment and consequently classify the Hyperspectral data. It may convince us to utilize the spatial features as well as the spectral characteristics of data to gain better classification results. In this paper we propose a Pixon-based image segmentation technique. We also apply a PDE-based smoothing algorithm to construct larger segments which are more homogenous. The resulted segment maps are then fed into the SVM classifier and the final thematic class maps are produced. The results of applying the proposed method on well-known Hyperspectral datasets imply that using the gained segments instead of pixels in the classification step leads to a considerable compression ratio as well as significant improvements in the classification accuracy and validity.
Starting Page	436
Ending Page	440
File Size	285787
Page Count	5
File Format	PDF
e-ISBN	9781479953592
DOI	10.1109/ISTEL.2014.7000743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-09
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Image segmentation FCM Smoothing methods Hyperspectral Pixon Concept Feature extraction Remote Sensing SVM Classifier Hyperspectral imaging
Content Type	Text
Resource Type	Article

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