Title :
A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area
Author :
Mo, Dengkui ; Hui Lin ; Jiping Li ; Hua Sun ; Zhuo Zhang ; Xiong, Yujiu
Author_Institution :
Central South Univ. of Forestry & Technol., Changsha
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.
Keywords :
geophysical signal processing; image classification; image resolution; object detection; remote sensing; support vector machines; Landsat images; NDVI index layers; SVM-based change detection method; bi-temporal remote sensing images; forest area; land cover change detection; multispectral images; supervised classification; support vector machines; Educational institutions; Forestry; Kernel; Multispectral imaging; Polynomials; Remote monitoring; Remote sensing; Satellites; Support vector machine classification; Support vector machines;
Conference_Titel :
Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on
Conference_Location :
Adelaide, SA
Print_ISBN :
978-0-7695-3090-1
DOI :
10.1109/WKDD.2008.49
