NDLI: Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles

Content Provider	IEEE Xplore Digital Library
Author	Benqin Song Jun Li Dalla Mura, M. Peijun Li Plaza, A. Bioucas-Dias, J.M. Atli Benediktsson, J. Chanussot, J.
Copyright Year	1980
Abstract	In recent years, sparse representations have been widely studied in the context of remote sensing image analysis. In this paper, we propose to exploit sparse representations of morphological attribute profiles for remotely sensed image classification. Specifically, we use extended multiattribute profiles (EMAPs) to integrate the spatial and spectral information contained in the data. EMAPs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of structural information. Although the EMAPs' feature vectors may have high dimensionality, they lie in class-dependent low-dimensional subpaces or submanifolds. In this paper, we use the sparse representation classification framework to exploit this characteristic of the EMAPs. In short, by gathering representative samples of the low-dimensional class-dependent structures, any given sample may by sparsely represented, and thus classified, with respect to the gathered samples. Our experiments reveal that the proposed approach exploits the inherent low-dimensional structure of the EMAPs to provide state-of-the-art classification results for different multi/hyperspectral data sets.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	5122
Ending Page	5136
Page Count	15
File Size	2736208
File Format	PDF
ISSN	01962892
Volume Number	52
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Vectors Feature extraction Hyperspectral imaging Dictionaries Kernel sparse representation Extended multiattribute profiles (EMAPs) remote sensing image classification
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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