Abstract
Abstract. 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.
Highlights
It is significant to map major agricultural producing areas for high-yield agriculture management and global and national geographical conditions monitoring
The Support Vector Machine (SVM) classifier is compared with AdaTree WL decision tree classifier in this study
This paper proposed a fast method of object-oriented automatic classification and it was an integration of image segmentation and the classifier of AdaTree WL
Summary
It is significant to map major agricultural producing areas for high-yield agriculture management and global and national geographical conditions monitoring. Remote sensing imagery is widely applied in land cover information extraction and agricultural production. Pixel-based classifiers are dominant in land cover mapping by using low and medium resolution remote sensing images for regional and global monitoring. With the development and application of high resolution remote sensing sensors, object-oriented classification algorithms are extensively utilized in regional land cover mapping and precision agriculture monitoring (Myint, 2011; Laliberte, 2004). One single classification algorithm cannot meet the need of quick land cover mapping with high efficiency, by utilizing high resolution remote sensing imagery with a large amount of information including spectral, structural and texture features
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