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Kalman Filter-Based Approaches to Hyperspectral Signature Similarity and Discrimination

机译：基于卡尔曼滤波的高光谱特征相似度和鉴别方法

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摘要

Kalman filter has been widely used in statistical signal processing for parameter estimation. Recently, a Kalman filter-based approach to spectral unmixing, referred to as Kalman filter-based linear unmixing (KFLU) was also developed for mixed pixel classification. However, its applicability to estimation and discrimination for hyperspectral signature characterization has not been explored where a hyperspectral signature is defined as a vector on a range of contiguous optical wavelengths of interest. This paper presents a new application of Kalman filtering in hyperspectral signature similarity and discrimination. In particular, it develops a Kalman filter-based signature estimator from which two Kalman filter-based discriminators can be derived for signature similarity and discrimination. The developed Kalman filter-based discriminators utilize a state equation to characterize a hyperspectral signature and a measurement equation to describe another hyperspectral signature, while the developed Kalman filter-based estimator makes use of state and measurement equations to describe the true signature and the observable signature respectively. The least squares error resulting from the Kalman filter-estimated hyperspectral signature is then used as the power for hyperspectral signature similarity and discrimination. Experimental results demonstrate that such Kalman filter-based discriminators are more effective than commonly used spectral similarity measures such as spectral angle mapper (SAM) or Euclidean distance.
机译：卡尔曼滤波器已广泛用于参数估计的统计信号处理中。最近，还开发了一种基于卡尔曼滤波器的频谱分解方法，称为基于卡尔曼滤波器的线性分解（KFLU），用于混合像素分类。然而，在将高光谱签名定义为感兴趣的连续光波长范围内的矢量的情况下，尚未探索其对高光谱签名表征的估计和判别的适用性。本文提出了卡尔曼滤波在高光谱特征相似度和鉴别中的新应用。特别是，它开发了基于卡尔曼滤波器的签名估计器，可以从中得出两个基于卡尔曼滤波器的鉴别器，以进行签名相似性和鉴别。发达的基于卡尔曼滤波器的鉴别器利用状态方程来表征高光谱特征，而测量方程则描述另一种高光谱特征，而发达的基于卡尔曼滤波器的估计器则利用状态和测量方程来描述真实的特征和可观测的特征。分别。然后，将由卡尔曼滤波器估计的高光谱签名产生的最小二乘误差用作高光谱签名相似性和区分力。实验结果表明，这种基于卡尔曼滤波器的鉴别器比诸如频谱角映射器（SAM）或欧几里德距离之类的常用频谱相似性度量更为有效。

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