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標題: | 結合光譜與空間特徵之高光譜影像物件分類 Hybrid Feature Extraction for Object-based Hyperspectral Image Classification |
作者: | Ting-Yi Li 李庭誼 |
指導教授: | 徐百輝(Pai-Hui Hsu) |
關鍵字: | 高光譜影像,光譜小波分解,空間紋理特徵,物件導向分類, Hyperspectral Remote Sensing,Hybrid Feature Selection,Object-Based Image Analysis (OBIA),Classification, |
出版年 : | 2011 |
學位: | 碩士 |
摘要: | 高光譜影像具有數十至數百個波段數,光譜資訊豐富且細緻,然而於實際應用時卻容易面臨資訊過多處理不易,以及分類時常易發生訓練樣本數不足的問題。另一方面,隨著遙測影像空間解析度之提升,影像中通常含有豐富之紋理(texture)資訊,能夠有效提升影像之分類精度,例如GLCM(gray level co-occurrence matrix)即為傳統常用的統計紋理分析方式,然而其卻有計算量大,計算時須降低影像色階數而導致影像光譜資訊流失等問題,故不適合用於高光譜影像之紋理分析。另外紋理的計算通常係以影像區塊或影像物件為基本單元,相較於傳統逐像元分類(pixel-based classification)方法,物件式的影像分析方法(object-based image analysis, OBIA)不會有邊緣破碎及椒鹽現象等問題。為有效解決上述有關高光譜影像分類所面臨之問題,本研究針對高光譜影像提出一套結合光譜特徵與空間紋理特徵的物件導向分類流程。首先,對高光譜影像進行小波光譜分解以縮減維度,萃取出數個有效的光譜特徵,接著對所萃取出之光譜特徵進行空間紋理特徵之計算,得到混和特徵,並以混合特徵選取之方式,選出適於分類的混合特徵組,最後,針對混合特徵組進行物件導向分類。實驗證實此套影像分類流程能夠達到約94%的分類精度,並且值得注意的是,此分類處理流程能夠大幅提升類別間分離度低的影像之光譜特徵萃取後的分類精度,成果顯示最高可提升分類精度約達20%。 The purpose of feature extraction is to reduce the dimensionality of hyperspectral images to solve classification problems caused by limited training samples. In this study, a hybrid feature extraction method which integrates spectral features and spatial features simultaneously is proposed. Firstly, the spectral-feature images are calculated along the spectral dimension of hyperspectral images using wavelet decomposition because wavelet has been proven effective in extracting spectral features. Secondly, ten different kinds of spatial-features, which are calculated along the two spatial dimensions of hyperspectral images, are implemented on the wavelet spectral-feature images. Then a feature selection method based on the optimization of class separability is performed on the extracted spectral-spatial features to get the hybrid features which could be suitable for classification applications. In this study, the object-based image analysis (OBIA) is used for hyperspectral image classification. The experiment results showed that the overall accuracy for the classification of a real hyperspectral data set using our proposed approach could reach approximately 94%. Moreover, it is worth mentioning that the hybrid features and OBIA classification could significantly rise the overall accuracy of hyperspectral images which contain poor separability between classes, after the spectral features were extracted. The experiment result also showed that the overall accuracy would go up by 20% by using our proposed approach on hyperspectral images with poor class separability. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43498 |
全文授權: | 有償授權 |
顯示於系所單位: | 土木工程學系 |
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