HYPERSPECTRAL IMAGE CLASSIFICATION AND DIMENSIONALITY REDUCTION - AN ORTHOGONAL SUBSPACE PROJECTION APPROACH
HYPERSPECTRAL IMAGE CLASSIFICATION AND DIMENSIONALITY REDUCTION - AN ORTHOGONAL SUBSPACE PROJECTION APPROACH
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DOI：
10.1109/36.298007
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发表时间：
1994-07-01
影响因子：
8.2
通讯作者：
CHANG, CI
中科院分区：
文献类型：
--
作者：
HARSANYI, JC;CHANG, CI
大多数高光谱图像的应用需要实现两个基本目标的处理技术：1)检测和分类场景中每个像素的组成材料；2)在不丢失关键信息的情况下，减少数据量/维数，使其能够被人类分析人员有效地处理和吸收。在本文中，我们描述了一种技术，它可以同时降低数据维数，抑制不希望的或干扰的光谱特征，并检测感兴趣的光谱特征的存在。基本概念是将每个像素向量投影到与不想要的签名正交的子空间上。该操作是最小二乘意义上的最优干扰抑制过程。一旦干扰签名被消除，将残差投影到感兴趣的签名上，可以最大化信噪比，并得到代表感兴趣签名分类的单分量图像。正交子空间投影（OSP）算子可以扩展到k个感兴趣的特征，从而降低k的维数，同时对高光谱图像进行分类。该方法既适用于纯光谱像素，也适用于混合像素。
Most applications of hyperspectral imagery require processing techniques which achieve two fundamental goals: 1) detect and classify the constituent materials for each pixel in the scene; 2) reduce the data volume/dimensionality, without loss of critical information, so that it can be processed efficiently and assimilated by a human analyst.In this paper, we describe a technique which simultaneously reduces the data dimensionality, suppresses undesired or interfering spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel vector onto a subspace which is orthogonal to the undesired signatures. This operation is an optimal interference suppression process in the least squares sense. Once the interfering signatures have been nulled, projecting the residual onto the signature of interest maximizes the signal-to-noise ratio and results in a single component image that represents a classification for the signature of interest. The orthogonal subspace projection (OSP) operator can be extended to k signatures of interest, thus reducing the dimensionality of k and classifying the hyperspectral image simultaneously. The approach is applicable to both spectrally pure as well as mixed pixels.