NDLI: Cloud classification in NOAA AVHRR imageries using spectral and textural features

Content Provider	Springer Nature Link
Author	Kaur, Rashpal Ganju, A.
Copyright Year	2008
Abstract	Clouds contribute significantly to the formation of many of the natural hazards. Hence cloud mapping and its classification becomes a major component of the various physical models which are used for forecasting natural hazards. The problem of cloud data classification from NOAA AVHRR (advance very high resolution radiometer) satellite imagery using image transformation techniques is considered in this paper. The singular value decomposition (SVD) scheme is used to extract the salient spectral and textural features attributed to satellite snow and cloud data in visible and IR channels. The goals of this paper are to discriminate between clear sky and clouds in an 8 × 8 pixel array of 1.1 km AVHRR data. If clouds are present then classify them as low, medium or high range. This scheme can effectively segregate clouds and non-cloud features in the visible and IR bands of the imagery. It can also classify clouds as low, medium or high range with a success rate of 70–90%. Computer-based snow and cloud discrimination and automatic cloud classification system will help the forecaster in various climatological applications, viz., energy balance estimation, precipitation forecasting, landslide forecasting, weather forecasting and avalanche forecasting etc.
Starting Page	167
Ending Page	174
Page Count	8
File Format	PDF
ISSN	0255660X
Journal	Journal of the Indian Society of Remote Sensing
Volume Number	36
Issue Number	2
e-ISSN	09743006
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-03-08
Publisher Place	India
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	NOAA AVHRR Cloud Classification Texture Remote Sensing/Photogrammetry Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Geography, Planning and Development

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