Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site
Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site
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DOI：
10.1016/j.isprsjprs.2007.08.007
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发表时间：
2008-03-01
影响因子：
12.7
通讯作者：
Karteris, Michael
中科院分区：
文献类型：
--
作者：
Mallinis, Georgios;Koutsias, Nikos;Karteris, Michael
一个多尺度，基于对象的分析快鸟卫星图像已经进行了描绘在希腊北方的天然森林森林植被多边形。在多分辨率分割之后，开发了分类树，并使用最近邻分类器将图像片段分配给类别进行比较。此外，还计算了由空间关联局部指标得到的纹理图像，并将其用于改进分类，当纹理图像被考虑在分类序列中时，获得了最好的结果，但最终地图的准确率不超过80%。分类树产生了更好的结果比最近邻算法。总的来说，基于对象的分类方法既有优点，也有局限性，在实际用于绘制地中海森林生态系统地图之前必须考虑到这一点。(C)2007年国际摄影测量和遥感学会。（摄影测量和遥感学会）。Elsevier B.V.出版，保留所有权利。
A multi-scale, object-based analysis of a Quickbird satellite image has been carried out to delineate forest vegetation polygons in a natural forest in Northern Greece. Following a multi-resolution segmentation, a classification tree was developed and compared using a nearest neighbour classifier for the assignment of image segments to classes. Additionally, texture images derived from local indicators of spatial association were calculated and used to improve the classification.The best results were obtained when texture images were considered in the classification sequence, however, the accuracy of the final map did not exceed 80%. The classification tree yielded better results than the nearest neighbour algorithm. Overall, the object-based classification approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping Mediterranean forest ecosystems. (C) 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.