NDLI: Classification of remote sensing data using fast learning neural networks and topology selection algorithms

Content Provider	IEEE Xplore Digital Library
Author	Dawson, M.S. Amar, F. Manry, M.T. Rawat, V. Fung, A.K.
Copyright Year	1994
Description	Author affiliation: Wave Scattering Res. Center, Texas Univ., Arlington, TX, USA (Dawson, M.S.; Amar, F.; Manry, M.T.; Rawat, V.; Fung, A.K.)
Abstract	The classification of remote sensing data has been a topic of interest since the early 70s. Recent studies have demonstrated that neural networks can be effective when used as a classifier of remote sensing data. One problem encountered using neural networks, though, is the time required to train the networks. This amount of time is directly related to the size of the network as well as to the amount of data used during the training. Earlier work demonstrated that a fast learning (FL) training algorithm provided a means to efficiently train neural networks in a fraction of the time required using conventional back-propagation learning algorithms. Additional decreases in the training time can be achieved by minimizing the network topology and analyses of the information content of the data. In this study, a fast learning (FL) neural network (NN) with optimized topology is applied to the problem of classification. Recently, new methods have been devised to determine the optimal size of the network required for a given set of data. The classification of remote sensing data using NNs trained with the FL and topology selection methods is examined. Several different SAR scenes are used to illustrate the technique. It is shown that the methods used are effective at classifying different land types while, at the same time, minimizing the size of the required networks and the time required for training the networks. The minimization of topology size, in addition to reducing training times, also reduces the amount of time for processing additional data.
Starting Page	1410
Ending Page	1412
File Size	280927
Page Count	3
File Format	PDF
ISBN	0780314972
DOI	10.1109/IGARSS.1994.399454
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-08-08
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Neural networks Network topology Polynomials Scattering Information analysis Layout Guidelines Neurons Signal analysis
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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