NDLI: Application of Self-Organizing Maps at Change Detection in Amazon Forest

Content Provider	IEEE Xplore Digital Library
Author	Mendes Mota, R.L. Ramos, A.C.B. Shiguemori, E.H.
Copyright Year	2014
Description	Author affiliation: Inst. for Adv. Studies, São José dos Campos, Brazil (Shiguemori, E.H.) \|\| Fed. Univ. of Itajuba, Itajubá, Brazil (Mendes Mota, R.L.; Ramos, A.C.B.)
Abstract	This paper presents a change detection of aerial images algorithm based on Kohonen self-organizing maps. In this study, a set of images from different period of time from Tapajós National Forest at Amazon forest in Pará, Brazil, has been employed. As input of the self-organizing maps, used as an unsupervised neural network, it was obtained features from the images using three different size of square windows and as output, a map of 16 colors, as 16 groupings. The results are obtained through the comparison between the outputs obtained with these different sizes of windows.
Starting Page	371
Ending Page	376
File Size	499312
Page Count	6
File Format	PDF
ISBN	9781479931873
e-ISBN	9781479931880
DOI	10.1109/ITNG.2014.41
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Self-organizing feature maps computational vision digital image processing Satellites Image color analysis Kohonen maps Image edge detection Neurons Feature extraction change detection Amazon forest
Content Type	Text
Resource Type	Article

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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
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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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Application of Self-Organizing Maps at Change Detection in Amazon Forest