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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/74472


    Title: Using object-based classification to detect landslides sites using high resolution aerial images
    Authors: Kung, Fan-En;Jan, Jan J.-F.;Shao, Y.-C.;Li, M.-Y.;Yeh, K.-S.;Chen, L.-H.
    孔繁恩
    Contributors: 地政學系
    Keywords: Aerial images;Classification approach;Classification methods;High-resolution aerial images;Hybrid classification;Object-based classifications;Segmentation techniques;Spatial resolution;Image classification;Landslides;Pixels;Remote sensing;Image segmentation
    Date: 2012
    Issue Date: 2015-04-10 16:34:39 (UTC+8)
    Abstract: A variety of image classification methods have been applied in satellite images analysis, including supervised, unsupervised, and hybrid classifications. However, many researches indicated that traditional pixel-based classification approaches often resulted in less satisfactory outcome when applied to high resolution aerial image data. The main reason is that pixel-based approaches often raise over-classification problem as the spatial resolution of images increases. The objective of this study is to use object-based classification method to detect landslides sites in aerial images acquired by Z/I DMC and Leica ADS40. Firstly, multi resolution segmentation technique will be applied to segment image into regions that correspond to various areas of interest. Then each region will be classified into appropriate land cover types by using different kinds of indexes which are defined in this study, and the landslide sites will be identified. Finally, the result of landslides sites can show that the object-based classification is a good method for extracting the areas of landslides.
    Relation: 33rd Asian Conference on Remote Sensing 2012, ACRS 2012
    Data Type: conference
    Appears in Collections:[地政學系] 會議論文

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