高光譜影像特徵萃取方法之探討

Feature Extraction for HyperSpectral Images

徐百輝(Pai-Hui Hsu) ； 曾義星(Yi-Hsing Tseng)

《航測及遙測學刊》 5卷2期 (2000/06) Pp. 1-14

https://doi.org/10.6574/JPRS.2000.5(2).1

摘要

與傳統多光譜影像比較，高光譜影像提供了更豐富且細緻的光譜資訊，理論上應該可以提升影像分類的精度。然而若將處理多光譜影像的理論及分類方法應用於高光譜影像中，所獲得的成果卻不如預期中理想。首先是高光譜龐大的資料量造成處理效率降低；而在分類精度上，隨著資料維度的增加，卻沒有足夠的訓練樣本數目以提升樣本統計量的估計精度，造成最後的分類精度降低。為了解決高維度資料所帶來的問題，特徵萃取成為高光譜資料分類之前所必須進行的工作。本文綜合整理一些既有的特徵萃取演算法，並從訊號處理的觀點出發提出兩種新的特徵萃取方法。最後對各種特徵萃取方法的結果實際進行分類及比較，期能找出一良好的特徵萃取方法，使得對於高光譜影像分類精度的增進能有所助益。

關鍵字

高光譜影像 ； 特徵萃取 ； 主軸分析 ； 傅立葉轉換 ； 小波轉換

並列摘要

Comparing to the traditional multispectral images, hyperspectral images include richer and finer spectral information than the images we can obtain before. Theoretically, using hyper-spectral images should increase our abilities in classifying land use/cover types. However, when traditional classification technologies are applied to process hyper-spectral images, people are usually disappointed at the consequences of low efficiency, needing a large amount of training data, and hard improvement of classification accuracy. In order to solve this problem, our attention in the paper is focused on dimensionality reduction by feature selection or ex-traction. In this paper, we propose two new methods for feature extraction. The Two methods based on Frequency Space are Fourrier Spectrum Feature Extraction and Wavelet Decomposition Feature Extraction. We also compare with other feature extraction methods which have been developed from some other proposed papers. Finally, a practical AVIRIS data are analyzed to illustrate our discovery and test to show the efficiency of the new feature extraction methods.

並列關鍵字

Hyperspectral Data ； Feature Extraction ； Principal Component Transform ； Fourier Transform ； Wavelet Decomposition