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Spectral Feature Extraction of Hyperspectral Images Using Wavelet Transform

小波轉換應用於高光譜影像光譜特徵萃取之研究

Abstracts


成像光譜儀所擷取的高光譜影像具有豐富且細緻的地物光譜資訊,此特性對於提升地物辨識能力及土地使用分類精度將有所助益。然而若以傳統的統計分類方法對高光譜影像進行分類時,其高維度的特性要求必須有大量的訓練樣本數目,同時大批的資料量亦降低了計算效率,分類精度也沒有顯著地提升。主要的問題在於當統計方法應用於高維度資料時所產生的「維度詛咒」現象。為了解決這個問題,本研究著重在光譜資料維度的降低,特徵萃取即為降低資料維度的一種方式,其基本觀念為去除較不重要的光譜資訊,只保留有用的特徵以降低高光譜資料的維度。本研究主要提出數種以小波理論為基礎的特徵萃取方法,以獲得對高光譜影像分類有用之光譜特徵。小波轉換為新一代的數學分析工具,其多層解析度及時不變的特性,使得其具有偵測局部訊號結構的能力。首先,我們先對高光譜影像進行離散小波或小波包轉換以獲得一組小波係數,之後再根據所設計的判斷法則選出有利於分類的特徵。因為特徵選取時係在維度較低的子空間中進行,因此有限樣本數目及「維度詛咒」等問題皆可以獲得有效的解決。本研究以兩組高光譜資料來測試所提小波轉換應用於光譜特徵萃取之有效性。實驗結果顯示以小波理論所萃取出的光譜特徵確實可以有效降低高光譜影像的資料維度,同時保持影像分類之精度。

Parallel abstracts


The rich and detailed spectral information provided by hyperspectral images can be used to identify and quantify a large range of surface materials which cannot be identified by multispectral images. However, the classification methods that have been successfully applied to multispectral data in the past are not as effective as for hyperspectral data. The main problem is that the training data set does not increase corresponding to the increase of dimensionality of hyperspectral data. Actually, the problem of the ”curse of dimensionality” emerges when a statistical classification method applied to hyperspectrál data. A simpler, but sometimes very effective way of dealing with hyperspectral data is to reduce the number of dimensionality. This can be done by feature extraction that a small number of salient features are extracted from the hyperspectral data when confronted with a limited set of training samples. In this study, several methods based on the wavelet transforms are developed to extract useful features for classification. Firstly, wavelet or wavelet packet transforms are implemented on the hyperspectral images and a sequence of wavelet coefficients is produced. Then, a simple feature selection procedure associated with a criterion is used to select the effective features for classification. Because the wavelet-based feature extraction optimizes the criterion in a lower dimensional space, the problems of limited training sample size and the curse of dimensionality can be avoided. Finally, two AVIRIS data sets are used to test the performance of the proposed wavelet-based methods. The experiment results show that the wavelet-based methods perform well for dimensionality reduction and also be effective for classification.

Cited by


Yang, H. H. (2007). 應用小波神經網路於高光譜影像分類 [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2007.00400

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