Implementation of real-time constrained linear discriminant analysis to remote sensing image classification
Implementation of real-time constrained linear discriminant analysis to remote sensing image classification
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DOI：
10.1016/j.patcog.2004.09.008
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发表时间：
2005-04-01
影响因子：
8
通讯作者：
Nekovei, R
中科院分区：
文献类型：
--
作者：
Du, Q;Nekovei, R
在本文中，我们调查的实时约束线性判别分析（CLDA）方法的遥感图像分类的实际实施问题。具体而言，有两个问题需要解决：（1）什么是最好的实现方案，产生最低的芯片设计复杂度与可比的分类性能。（2）如何扩展CLDA算法用于多光谱图像分类。关于数据维度的两个限制必须放宽。一个是实时高光谱图像分类。其中接收的用于分类的线性独立像素的数量必须大于数据维度（即，频谱带的数量），以便为分类器生成非奇异样本相关矩阵R，并且放宽该限制可以帮助解决上述第一个问题。二是多光谱图像分类。其中待分类的类别的数目不能大于数据维度，并且放宽此限制可有助于解决前述第二个问题。前者可以通过引入样本相关矩阵的伪逆来实现R-1自适应。而后者通过经由手乘操作扩展数据维度来处理。分类性能使用这些修改进行实验，以证明其可行性。所有这些调查导致一个详细的ASIC芯片设计方案的实时CLDA算法适用于高光谱和多光谱图像。提出的技术。解决这两个维度的局限性，对遥感图像开发中几种常用的检测和分类方法的实时实现具有指导意义。(C)2004模式识别学会。由爱思唯尔有限公司出版。保留所有权利。
In this paper, we investigate the practical implementation issues of the real-time constrained linear discriminant analysis (CLDA) approach for remotely sensed image classification. Specifically, two issues are to be resolved: (1) what is the best implementation scheme that yields lowest chip design complexity with comparable classification performance. and (2) how to extend CLDA algorithm for multispectral image classification. Two limitations about data dimensionality have to be relaxed. One is in real-time hyperspectral image classification. where the number of linearly independent pixels received for classification must be larger than the data dimensionality (i.e., the number of spectral bands) in order to generate a non-singular sample correlation matrix R for the classifier, and relaxing this limitation can help to resolve the aforementioned first issue. The other is in multispectral image classification. where the number of classes to be classified cannot be greater than the data dimensionality, and relaxing this limitation can help to resolve the afore mentioned second issue. The former can be solved by introducing a pseudo inverse initiate of sample correlation matrix for R-1 adaptation. and the latter is taken care, of by expanding the data dimensionality via the operation of hand multiplication. Experiments on classification performance using these modifications are conducted to demonstrate their feasibility. All these investigations lead to a detailed ASIC chip design-scheme for the real-time CLDA algorithm suitable to both hyperspectral and multispectral images. The proposed techniques. to resolving these two dimensionality limitations are instructive to the real-time implementation of several popular detection and classification approaches in remote sensing image exploitation. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.