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|標題:||A genetic algorithm (GA) based automated classifier for remote sensing imagery||作者:||Yang, M.D.
|關鍵字:||sensed data;segmentation;landslide;clusters;validity||Project:||Canadian Journal of Remote Sensing||期刊/報告no：:||Canadian Journal of Remote Sensing, Volume 33, Issue 3, Page(s) 203-213.||摘要:||
Conventional unsupervised classification divides all pixels within an image into corresponding classes based on the distance between pixels and the cluster centres. The number of classes must be selected a priori but is seldom ascertainable with little information. To analyze a large dataset, such as a remote sensing dataset, requires an automatic unsupervised classifier which needs no human effort during the process of image clustering. A genetic algorithm (GA) is adopted to search the cluster centres and choose a suitable cluster number for digital images to overcome the disadvantages of the conventional unsupervised classifier. The GA-based automated classifier was executed on several test images for validity and SPOT satellite imagery for practical application. The satellite images classified by the GA-based classifier and iterative self-organizing data analysis technique (ISODATA) were compared with a classified result through a supervised classification. According to the estimation of classification accuracy by error matrices and kappa statistic, the GA-based classifier performed better than the unsupervised ISODATA and as good as a supervised classifier, even without manipulation by an analyst. A modified GA-based classifier using maximum likelihood (represented by the z score) as a clustering criterion was also proposed and proven to be capable of performing automatically as well as a supervised classifier.
|Appears in Collections:||土木工程學系所|
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