應用經驗模分解技術進行高光譜影像之雜訊過濾

Noise Reduction of Hyperspectral Image Using Emperical Mode Decomposition

張立雨(L. Y. Chang) ； 陳繼藩(C. F. Chen)

《航測及遙測學刊》 14卷2期 (2009/06) Pp. 115-125

https://doi.org/10.6574/JPRS.2009.14(2).3

摘要

高光譜遙測影像一般而言較其他遙測資料可提供更爲詳盡之光譜資訊，因此在用於辨識各種不同地物時有助於精度之提升。而對於此種影像來說，線性光譜混合模型(Linear Spectral Mixing Model)爲常用之模式來對影像中之光譜值與其對應各種地物間之光譜反應加以描述，並且進而反演得到所對應各種地物之含量。基本上，線性光譜混合模型可利用最小二乘法加以求解來達到解算各種地物之含量之目的，並且同時使得在光譜上所產生之誤差達到最小之狀態。理論上，若能有效過濾高光譜影像本身所具有之雜訊時，求解所得之地物之含量在精度上應會有所提升。通常對高光譜遙測影像而言，因大氣中各種分子在不同波段上具有其特定之吸收與散射特性，因此會對感測器接收來自於地物之輻射能量造成增減而產生雜訊。在本研究中使用經驗模分解法(Empirical Mode Decomposition, EMD)來對原始高光譜影像進行分解組合，並且進而過濾其雜訊，以期能在最小二乘求解時能降低其殘差，以達到提高求解地物含量精度之目的。由研究成果顯示，在模擬的高光譜影像資料上進行雜訊過濾後，求解所得之含量誤差較未過濾前有顯著之降低。此外若直接應用所提方法於真實影像上時，就求解過程所得之殘差上而言，雜訊過濾後所得結果之殘差亦較未濾前爲低。

關鍵字

高光譜影像 ； 雜訊過濾 ； 經驗模分解

並列摘要

Hyperspectral images are able to provide the detailed spectral information necessary for the discrimination of different land targets in various kinds of remotely sensed images. The linear spectral mixing model is a widely used discrimination method for modeling the variety of multiple land targets in hyperspectral images. Basically, the linear spectral mixing model is solved by least squares adjustment to acquire the minimal-error solutions. It is believed that the reduction of the noise that inherently exists in each spectral band of the hyperspectral image can increase the discrimination accuracy. In general, the noise recorded in the hyperspectral sensor is caused by the absorption or scattering effect of the atmospheric particles during the energy transportation process. In this study, a noise filtering preprocessing based on empirical mode decomposition (EMD) is proposed. The purpose is to reduce the inherent noise and further minimize the residuals of least squares solutions for hyperspectral images. The test results with simulated images indicate that the proposed noise filtering process can effectively decrease the abundance errors. Moreover, in the experiment with real image, the residuals of least square adjustment for noise reduced data set are also smaller than that for original data set.

並列關鍵字

Noise Reduction ； Hyperspectral Images ； Empirical Mode Decomposition