NDLI: The method research of city vegetation information abstraction based on high-resolution IKONOS image

Content Provider	IEEE Xplore Digital Library
Author	Bao Yansong Li Xiaowen Wang Changzuo Wang Jihua Liu Liangyun
Copyright Year	2004
Description	Author affiliation: Res. Center for Remote Sensing & GIS, State Key Lab. of Remote Sensing Sci., Beijing, China (Bao Yansong; Li Xiaowen; Wang Changzuo)
Abstract	In the first part of this article, traditional methods for deriving city vegetation information through remotely sensed images, the study of the area's characteristics and the research flow are introduced. In this article, two main issues are studied, one is city shadow correction, the other is the classifying method of city vegetation through remote sensing images. The biggest problem of high-resolution images is the shadow of high buildings. City shadows decrease the classification accuracy, so a shadow adjustment method must be studied. In this article, we analyze the radiation correction model, and acquire the shadow adjustment model. The model's parameters can be calculated through the image pixel values. The result of shadow correction shows that the model can correct for city shadow. After shadow correction, the vegetation's pixel value in the shadow is similar to the pixel value of vegetation in other areas. Shadow correction increases classification accuracy. In the second part of this article, three methods are studied to derive city vegetation information, including the vegetation index method, a back propagation neural network method, and a texture method. Finally, the three methods' classification accuracies are calculated and appraised. A conclusion is drawn, which is that the texture classification method is a good classification method. The accuracy of texture classification method can reach 91.53%.
Starting Page	2720
Ending Page	2723
File Size	999796
Page Count	4
File Format	PDF
ISBN	0780387422
DOI	10.1109/IGARSS.2004.1369863
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cities and towns Vegetation mapping Image resolution Remote sensing Data mining Geographic Information Systems Neural networks Appraisal Geography Laboratories
Content Type	Text
Resource Type	Article

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