Projection Pursuit-Based Dimensionality Reduction for Hyperspectral Analysis

نویسندگان

  • Haleh Safavi
  • Chein-I Chang
  • Antonio J. Plaza
چکیده

Dimensionality Reduction (DR) has found many applications in hyperspectral image processing. This book chapter investigates Projection Pursuit (PP)-based Dimensionality Reduction, (PP-DR) which includes both Principal Components Analysis (PCA) and Independent Component Analysis (ICA) as special cases. Three approaches are developed for PP-DR. One is to use a Projection Index (PI) to produce projection vectors to generate Projection Index Components (PICs). Since PP generally uses random initial conditions to produce PICs, when the same PP is performed in different times or by different users at the same time, the resulting PICs are generally different in terms of components and appearing orders. To resolve this issue, a second approach is called PI-based PRioritized PP (PI-PRPP) which uses a PI as a criterion to prioritize PICs. A third approach proposed as an alternative to PI-PRPP is called Initialization-Driven PP (ID-PIPP) which specifies an appropriate set of initial conditions that allows PP to produce the same PICs as well as in the same order regardless of how PP is run. As shown by experimental results, the three PP-DR techniques can perform not only DR but also separate various targets in different PICs so as to achieve unsupervised target detection. H. Safavi (*) Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA e-mail: [email protected] C.-I Chang Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan e-mail: [email protected] A.J. Plaza Department of Technology of Computers and Communications, University of Extremadura, Escuela Politecnica de Caceres, Caceres, SPAIN e-mail: [email protected] B. Huang (ed.), Satellite Data Compression, DOI 10.1007/978-1-4614-1183-3_14, # Springer Science+Business Media, LLC 2011 287

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

انجام یک مرحله پیش پردازش قبل از مرحله استخراج ویژگی در طبقه بندی داده های تصاویر ابر طیفی

Hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. However, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. Various investigations indicate that the key problem that causes poor performance in the stochastic approaches t...

متن کامل

Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images

Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...

متن کامل

Projection Pursuit and Lowpass Filtering for Preprocessing of Hypespectral Images

Hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. However, the stochastic data analysis approaches, successfully applied to classification of multispectral data, are not as effective as those for hyperspectral data. Various investigations indicate that the key problem causing poor performance in the stochastic approaches...

متن کامل

About Classification Methods Based on Tensor Modelling for Hyperspectral Images

Denoising and Dimensionality Reduction (DR) are key issue to improve the classifiers efficiency for Hyper spectral images (HSI). The multi-way Wiener filtering recently developed is used, Principal and independent component analysis (PCA; ICA) and projection pursuit (PP) approaches to DR have been investigated. These matrix algebra methods are applied on vectorized images. Thereof, the spatial ...

متن کامل

2D Dimensionality Reduction Methods without Loss

In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were used to improve the performance of its predictive model, which was a support vector machine (...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012