以多時期與PCA+NDVI法改善地物分類之正確性與完整性

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dc.contributor.author張國楨zh_tw
dc.contributor.author田應平zh_tw
dc.contributor.author施孝謙zh_tw
dc.contributor.authorKuo-chen Changen_US
dc.contributor.authorYing-Ping Tianen_US
dc.contributor.authorHsiao-Chien Shihen_US
dc.date.accessioned2014-10-27T15:40:51Z
dc.date.available2014-10-27T15:40:51Z
dc.date.issued2012-11-??zh_TW
dc.description.abstract衛星影像分析是環境變遷監測的主要方法之一。結果的精確度與可信度深受所用影像的光譜解析度與地物光譜分辨率影響。以單一時期之原始影像進行分類，常因光譜雜訊與地物間光譜混淆情況而影響分類正確性。本研究以主成分分析(PCA) 去除原始影像雜訊，整合多時期影像增加影像光譜解析度以及地物間的辨別率來提升分類正確性。實驗區為社子島地區2005、2006、2007年多時期R、G、B、IR 影像，先行以PCA 萃取出可解釋量總和>95％之兩主成分，並進一步轉置回R、G、B、IR 影像，再加上各時期NDVI 進行地物分類。實驗結果顯示，經過PCA 處理，可增揚地物本身光譜特性。研究以多時期處理後之影像進行非監督式分類，顯示與單時期2007分類影像相比，本研究採用方法在改善地物分類之正確性及完整性上具有較佳效果。zh_tw
dc.description.abstractSatellite image analysis is one of the main methods of monitoring of environmental changes. The accuracy and credibility of the results depend on the spectral resolution of the imagery used and the spectral separability between features being monitored. If original imagery from single period is used to perform classification, the results are often affected by the noise of imagery itself and spectral similarity between different features. In this research, we proposed an improved solution for change detection that combines a Principle Component Analysis (PCA) process to remove noise and a multi-temporal process to increase spectral resolution. The study area is located on Shezi Island with imageries from 2005, 2006, 2007. The original imageries were processed with PCA to retain the first two major components which account for over 95% of total explanation. The resulting imageries were inversed by IPCA process back to 4 bands imageries. NDVI was calculated for each time period and stacked with IPCA imageries to create a new multi-temporal imagery for further unsupervised classification. The experimental results showed that the PCA process does enhance the spectral characteristic of features being monitored. An unsupervised classification process was applied to the multi-temporal imagery. The result was compared to that from 2007 and showed a significant improvement in both accuracy and completeness of the land cover classification.en_US
dc.identifierF669C5FC-B391-CD25-A9C0-0A438049D12Czh_TW
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/23928
dc.language中文zh_TW
dc.publisher地理學系zh_tw
dc.publisherDepartment of Geography, NTNUen_US
dc.relation(57)，49-60zh_TW
dc.relation.ispartof地理研究zh_tw
dc.subject.other地物分類zh_tw
dc.subject.other多時期影像zh_tw
dc.subject.otherPCAzh_tw
dc.subject.other非監督式分類zh_tw
dc.subject.other完整性zh_tw
dc.subject.otherLand Cover Classificationen_US
dc.subject.otherMulti-temporal Imageryen_US
dc.subject.otherPCAen_US
dc.subject.otherUnsupervised Classificationen_US
dc.subject.otherCompletenessen_US
dc.title以多時期與PCA+NDVI法改善地物分類之正確性與完整性zh-tw
dc.title.alternativeUsing Multi-temporal and PCA＋NDVI to Improve the Accuracy and Integrity of Land Cover Classificationzh_tw

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