NDLI: A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area

Content Provider	IEEE Xplore Digital Library
Author	Dengkui Mo Hui Lin Jiping Li Hua Sun Zhuo Zhang Yujiu Xiong
Copyright Year	2008
Description	Author affiliation: Central South Univ. of Forestry & Technol., Changsha (Dengkui Mo; Hui Lin)
Abstract	The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection in forest regions. Firstly, multidate remote sensing images are co-registered and we have stacked the NDVI index layers of two dates in red, green, blue bands composite to perform a supervised classification. Secondly, sample pixels were manually selected from changed and unchanged area to be used in the training stage. Thirdly, for each pixel SVM produces a single output through its decision function, high detection overall accuracy (>96%) and overall Kappa coefficient (>0.89) were achieved using two landsat images covering an 8-years period in study area. Lastly, SVM-based change detection with different kernel functions was compared using statistical evaluations.
Starting Page	209
Ending Page	212
File Size	688075
Page Count	4
File Format	PDF
ISBN	9780769530901
DOI	10.1109/WKDD.2008.49
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-01-23
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Satellites Multispectral imaging Support vector machine classification Forestry Educational institutions Polynomials Kernel Remote sensing Remote monitoring
Content Type	Text
Resource Type	Article

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