NDLI: Artificial intelligence for mixed pixel resolution

Content Provider	IEEE Xplore Digital Library
Author	Gupta, N. Panchal, V.K.
Copyright Year	2011
Description	Author affiliation: Bhagwan Parshuram Institute of Technology, GGSIPU, Delhi, India (Gupta, N.) \|\| Defence Terrain Research Laboratory, Delhi, India (Panchal, V.K.)
Abstract	Mixed pixels are usually the biggest reason for lowered success in classification accuracy. Aiming at the characteristics of remote sensing image classification, the mixed pixel problem is one of the main factors that affect the improvement of classification precision in image. How to decompose the mixed pixels precisely and effectively for multispectral/hyper spectral remote sensing images is a critical issue for the quantitative research. As Remote sensing data is widely used for the classification of types of land cover such as vegetation, water body thus Conflicts are one of the most characteristic attributes in satellite multilayer imagery. Conflict occurs in tagging class label to mixed pixels that encompass spectral response of different land cover on the ground element. In this paper we attempted to present a new approach for resolving the mixed pixels using Biogeography based optimization. The paper deals with the idea of tagging the mixed pixel to a particular class by finding the best suitable class for it using the concept of immigration and emigration.
Starting Page	2801
Ending Page	2804
File Size	413501
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Image resolution Biogeography Optimization Vegetation mapping Satellites Classification algorithms Bio-geography based optimization Decision Support Artificial Intelligence Mixed Pixel
Content Type	Text
Resource Type	Article

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