NDLI: *Miner: A Suit of Classifiers for Spatial, Temporal, Ancillary, and Remote Sensing Data Mining

Content Provider	IEEE Xplore Digital Library
Author	Vatsavai, R.R. Shekhar, S. Burk, T.E. Bhaduri, B.
Copyright Year	2008
Description	Author affiliation: Oak Ridge Nat. Lab., Oak Ridge (Vatsavai, R.R.)
Abstract	Thematic classification of multi-spectral remotely sensed imagery for large geographic regions requires complex algorithms and feature selection techniques. Traditional statistical classifiers rely exclusively on spectral characteristics, but thematic classes are often spectrally overlapping. The spectral response distributions of thematic classes are dependent on many factors including terrain, slope, aspect, soil type, and atmospheric conditions present during the image acquisition. With the availability of geo-spatial databases, it is possible to exploit the knowledge derived from these ancillary geo-spatial databases to improve the classification accuracies. However, it is not easy to incorporate this additional knowledge into traditional statistical classification methods. On the other hand, knowledge-based and neural network classifiers can readily incorporate these spatial databases, but these systems are often complex to train and their accuracy is only slightly better than statistical classifiers. In this paper we present a new suit of classifiers developed through NASA funding, which addresses many of these problems and provide a framework for mining multi-spectral and temporal remote sensing images guided by geo-spatial databases.
Starting Page	801
Ending Page	806
File Size	585922
Page Count	6
File Format	PDF
ISBN	0769530990
DOI	10.1109/ITNG.2008.243
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis supervised learning Spatial databases Data mining Remote sensing Image analysis Image databases weka markov random fields Clustering algorithms Soil clustering spatial data mining Remote monitoring Principal component analysis
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@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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