A Band Selection approach of Simulated Annealing Feature Uniformity for the Data Fusion of Hyperspectral and SAR Imageries
| Autor: | Jing-Yi Cheng, 陳靖怡 |
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| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 96 With the recent advances of state-of-the-art sensors, data initially developed in a few multispectral bands today can be now collected from several hundred hyperspectral and even thousands of ultraspectral bands. While images are continuously being acquired and archived, existing methodologies have proved inadequate for analyzing such large volumes of data. As a result, a vital demand exists for new concepts and methods to deal with high-dimensional datasets. In this paper,we fuse hyperspectral imaging and synthetic aperture radar imaging. We use Simulated annealing feature uniformity band selection (SAFU) from hyperspectral imaging feature extraction. Previously, scholars have put forward the “simulated annealing band selection” (SABS). In this paper, we propose a novel feature extraction method, called simulated annealing feature uniformity (SAFU) band selection approach to improve the computational and the precise performances of the “clustered eigenspace / feature scale uniformity transformation” (CE/FSUT) of SABS method for clustering the CE features. It takes advantage of the special characteristics of SA to concentrate the CE feature sets of different classes into the most common feature subspaces. A distance measure based on SAFU is then applied to decompose the similarity for land cover classification purposes. Compared with the CE/FSUT method, the SAFU can group the CE feature sets of each different class in the same orders and can unify the feature scales of each different CE feature set at the same time. It can simultaneously group highly correlated bands of each different class into the same CE feature sets with higher effectiveness but lower computational loads. To demonstrate the advantages of the proposed method, we compared several different configurations categorized by the parameters of constructing SA annealing schedule. The performance of the propose method is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) images and the Airborne Synthetic Aperture Radar (AIRSAR) images. Compared with conventional feature extraction techniques, SAFU evinced improved discriminatory properties, crucial to subsequent PBF classification. It made use of the potentially significant separability embedded in high-dimensional datasets to select a unique set of the most important feature bands. The experimental results showed that the proposed SAFU approach is effective and can be used as an alternative to the existing feature extraction method for the data fusion of hyperspectral data sets. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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