Change Detection of Satellite Image with Direct Multidate Classification
碩士 === 國立中央大學 === 土木工程研究所 === 87 === Recently,change detection from satellite images has been applied in various fields wildly. For example: the exploitation of the golf courses, the demarcations of the damageable zone after typhoons and the surveying of the crop growth. Satellite images sustain gre...
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ndltd-TW-087NCU000150862016-07-11T04:13:51Z http://ndltd.ncl.edu.tw/handle/37992842782556103885 Change Detection of Satellite Image with Direct Multidate Classification 多時段分類法應用於衛星影像變遷偵測之研究 Yang Shen 楊紳 碩士 國立中央大學 土木工程研究所 87 Recently,change detection from satellite images has been applied in various fields wildly. For example: the exploitation of the golf courses, the demarcations of the damageable zone after typhoons and the surveying of the crop growth. Satellite images sustain great help for monitoring the land change because of its characteristics of multispectral , period and digital. In this study, we will use the method of direct multidate classification for change detection. It combines the properties of multi-spectral and temporal variability of the satellite images. We classified all kinds of changed types from supervised fuzzy classification and compounded all training areas information by the method of perm. To avoid the errors from the atmospheric effect, we got the training areas from the first image and the second image severally. At last, we used the conception of the knowledge bases to shorten the computer time and increase the efficiency and the accuracy of change detection. This method has been tested by the simulated images and SPOT images. The results showed that it can detect the change of the areas with very high accuracy and automatic operation. C. F. Chen 陳繼藩 1999 學位論文 ; thesis 63 zh-TW |
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碩士 === 國立中央大學 === 土木工程研究所 === 87 === Recently,change detection from satellite images has been applied in various fields wildly. For example: the exploitation of the golf courses, the demarcations of the damageable zone after typhoons and the surveying of the crop growth. Satellite images sustain great help for monitoring the land change because of its characteristics of multispectral , period and digital.
In this study, we will use the method of direct multidate classification for change detection. It combines the properties of multi-spectral and temporal variability of the satellite images. We classified all kinds of changed types from supervised fuzzy classification and compounded all training areas information by the method of perm. To avoid the errors from the atmospheric effect, we got the training areas from the first image and the second image severally. At last, we used the conception of the knowledge bases to shorten the computer time and increase the efficiency and the accuracy of change detection.
This method has been tested by the simulated images and SPOT images. The results showed that it can detect the change of the areas with very high accuracy and automatic operation.
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C. F. Chen |
author_facet |
C. F. Chen Yang Shen 楊紳 |
author |
Yang Shen 楊紳 |
spellingShingle |
Yang Shen 楊紳 Change Detection of Satellite Image with Direct Multidate Classification |
author_sort |
Yang Shen |
title |
Change Detection of Satellite Image with Direct Multidate Classification |
title_short |
Change Detection of Satellite Image with Direct Multidate Classification |
title_full |
Change Detection of Satellite Image with Direct Multidate Classification |
title_fullStr |
Change Detection of Satellite Image with Direct Multidate Classification |
title_full_unstemmed |
Change Detection of Satellite Image with Direct Multidate Classification |
title_sort |
change detection of satellite image with direct multidate classification |
publishDate |
1999 |
url |
http://ndltd.ncl.edu.tw/handle/37992842782556103885 |
work_keys_str_mv |
AT yangshen changedetectionofsatelliteimagewithdirectmultidateclassification AT yángshēn changedetectionofsatelliteimagewithdirectmultidateclassification AT yangshen duōshíduànfēnlèifǎyīngyòngyúwèixīngyǐngxiàngbiànqiānzhēncèzhīyánjiū AT yángshēn duōshíduànfēnlèifǎyīngyòngyúwèixīngyǐngxiàngbiànqiānzhēncèzhīyánjiū |
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