NDLI: Exploiting Hyperspectral Hypertemporal Imagery with Feature Clustering for Invasive Species Detection

Content Provider	IEEE Xplore Digital Library
Author	Mathur, A. Bruce, L.M. Johnson, D.W. Robles, W.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS (Mathur, A.; Bruce, L.M.; Johnson, D.W.; Robles, W.)
Abstract	This paper presents a feature extraction method for exploiting hyperspectral hypertemporal data and applies the new method to the problem of invasive species detection. By definition, hyperspectral hypertemporal imagery is very high dimensional data, and dimensionality reduction will play a critical role in utilizing such data. We present a feature extraction method that takes advantage of the high correlation among elements of the spectral and temporal feature space. This high correlation can be attributed to the premise that the changes in the reflectance of closely spaced wavelengths do not always change dramatically over short periods of time. The proposed feature clustering method is based on the assumption that adjacent elements in the spectro-temporal feature space are highly correlated and can be grouped together to form lower- dimensional feature spaces. The proposed hyperspectral hypertemporal feature clustering method is tested and validated within an invasive vegetation detection application. The hypothesis is that as time progresses, the spectral response of different plant species change differently. Thus, there should be hyperspectral hypertemporal features that can be used to discriminate between the vegetative species. Additionally, the results of the feature clustering method can be used to determine which regions of the spectrum and which collection dates are optimum for the given invasives detection problem.
Starting Page	828
Ending Page	831
File Size	314766
Page Count	4
File Format	PDF
ISBN	0780395107
DOI	10.1109/IGARSS.2006.212
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Hyperspectral sensors Feature extraction Clustering methods Vegetation mapping Computer vision Training data Data mining Plants (biology) Data engineering
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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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