無參數加權特徵萃取對遙測影像目標偵測的應用

Nonparametric Weighted Feature Extraction for Target Detection in Remote Sensing

紀萬偉(Wan-Wei Chi) ； 任玄(Hsuan Ren)

《航測及遙測學刊》 12卷4期 (2007/12) Pp. 335-341

https://doi.org/10.6574/JPRS.2007.12(4).5

摘要

線性混合模式(Linear spectral mixture analysis)已經廣泛的被應用在遙測領域上，而最小平方誤差(Least Squares)是眾多有效處理線性混合模式的方法之一。雜訊在線性混合模式中的每一波段不一定是呈現獨立且相同分佈(Independent and Identical Distributed, (i.i.d))，而雜訊白化最小平方誤差(Noise Whitened Least Squares, (NWLS))藉由雜訊白化處理把雜訊分佈轉換成i.i.d.，並已經被證明能改善傳統最小平方誤差法的效能。然而，如何去估計出雜訊的共變異數矩陣仍然是一個重要的課題。目前已經有許多估計雜訊分佈的方法被提出來，包含空間的高通濾波器、頻率域的高通濾波器、正交子空間投影、主成份分析法和費雪線性區別法(Fisher's Linear Discriminant Analysis, (Fisher's LDA))。這些方法在雜訊是高斯分佈時都有很好的估計，但是當雜訊的分佈不是高斯分佈時，其效能不盡理想。這篇文章中我們採用無參數加權特徵萃取法(Nonparametric Weighted Feature Extraction, (NWFE))來估計雜訊的分佈並和前人提出的方法做比較。同時限制能量最小化法(Constrained Energy Minimization, (CEM))在遙測的目標物偵測上我們也加上權重改善CEM對目標光譜過於敏感的問題。

關鍵字

雜訊白化最小平方誤差法 ； 無參數加權特徵萃取 ； 限制能量最小化法

並列摘要

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing applications, and the Least Squares (LS) approach is one of the most effective methods for solving LSMA problem. Since the noise in LSMA from each band may not be independent and identical distributed (i.i.d.), it has been proven mathematically that the Noise Whitened Least Squares (NWLS) will outperform the original LS by making the noise i.i.d. with the noise whitening process. But how to estimate the noise covariance matrix is remain a challenge problem. Many methods have been proposed in the past including spatial high-pass filter, frequency domain high-pass filter, orthogonal subspace projection, principal component analysis and Fisher's Linear Discriminant Analysis (Fisher's LDA) based approach. They all perform well for Gaussian noise but encounter problems when the noise is ill distributed. In this study, we adopt Nonparametric Weighted Feature Extraction (NWFE) to estimate the noise distribution and compare the results. Furthermore, we also apply the weighted factor for Constrained Energy Minimization (CEM) to overcome its sensitivity problem to the target spectrum.

並列關鍵字

Noise Whitened Least Squares NWLS ； Nonparametric Weighted Feature Extraction NWFE ； Constrained Energy Minimization CEM