NDLI: Textural information of multitemporal ERS-1 and JERS-1 SAR images with applications to land and forest type classification in boreal zone

Content Provider	IEEE Xplore Digital Library
Author	Kurvonen, L. Hallikainen, M.T.
Copyright Year	1980
Abstract	The textural information of a multitemporal set of ERS-1 and JERS-1 synthetic aperture radar (SAR) images was studied with the first- and second-order statistical measures. These measures had a higher information value for the land-cover and forest type classification than the SAR image intensity. The multitemporal approach was beneficial for the application of the textural measures; the textural parameters significantly improved the classification of land-cover and forest types. Based on the SAR image texture, the overall classification accuracy for seven land-cover types was 65%, while with the SAR image intensity, the classification accuracy was 50%, respectively. In the forest type classification based on the SAR image texture and intensity, the overall classification accuracy for four forest types was 66%, while with the intensity, the accuracy was 40%, respectively. The weather and seasonal conditions had a significant effect on the textural information of SAR images. The best separability of the signatures and the best land-cover and forest type classification accuracy was achieved under summer conditions. The snow cover and arid conditions decreased the textural information of the SAR images.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	680
Ending Page	689
Page Count	10
File Size	579984
File Format	PDF
ISSN	01962892
Volume Number	37
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Synthetic aperture radar Fractals Vegetation mapping Image texture Testing North America Space technology Image segmentation Snow Radar imaging
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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