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

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008) Pub Date : 2008-01-23 DOI:10.1109/WKDD.2008.49

D. Mo, Hui Lin, Jiping Li, Hua Sun, Zhuo Zhang, Y. Xiong

引用次数: 7

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.

查看原文本刊更多论文

基于svm的森林地区双时相遥感影像变化检测方法

支持向量机对遥感多光谱图像进行分类的可靠性已经在各种研究中得到了验证。本文研究了它们在森林地区土地覆盖变化检测中的适用性。首先，对多日期遥感影像进行共配准，将两个日期的NDVI指数层叠加在红、绿、蓝复合波段上，进行监督分类;其次，从变化区域和未变化区域中手动选择样本像素点用于训练阶段。第三，SVM通过决策函数每像素产生一个输出，在研究区8年的两幅陆地卫星图像上获得了较高的检测总体精度(>96%)和总体Kappa系数(>0.89)。最后，对不同核函数下基于支持向量机的变更检测进行统计评价比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)

自引率

0.00%

发文量