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An Object-oriented Approach for Agrivultural Land Classification Using Rapideye Imagery : Volume Xl-7/W4, Issue 1 (26/06/2015)

By Sang, H.

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Book Id: WPLBN0004016064
Format Type: PDF Article :
File Size: Pages 4
Reproduction Date: 2015

Title: An Object-oriented Approach for Agrivultural Land Classification Using Rapideye Imagery : Volume Xl-7/W4, Issue 1 (26/06/2015)  
Author: Sang, H.
Volume: Vol. XL-7/W4, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


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Zhang, J., Zhai, L., An, F., & Sang, H. (2015). An Object-oriented Approach for Agrivultural Land Classification Using Rapideye Imagery : Volume Xl-7/W4, Issue 1 (26/06/2015). Retrieved from

Description: Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Haidian District, Beijing, China. With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery acquired on May 29 and August 9th of 2012 was used in this study to evaluate the potential of red edge band in agricultural land cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing redundant information improves the mapping precision over 9% for AdaTree. WL, and 5% for SVM, the accuracy is over 90% for both approaches. Time phase characteristic is much important in different agricultural lands, and it improves the classification accuracy 7% for AdaTree.WL and 6% for SVM.

An object-oriented approach for agrivultural land classification using rapideye imagery


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