NDLI: Particle Swarm Optimization-Based Impurity Function Band Prioritization Using Weighted Majority Voting for Feature Extraction of High Dimensional Data Sets

Content Provider	IEEE Xplore Digital Library
Author	Yang-Lang Chang Min-Yu Huang Ping-Hao Wang Tung-Ju Hsieh Jyh-Perng Fang Bormin Huang
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan (Yang-Lang Chang; Min-Yu Huang; Ping-Hao Wang; Tung-Ju Hsieh; Jyh-Perng Fang) \|\| Space Sci. & Eng. Center, Univ. of Wisconsin-Madison, Madison, WI, USA (Bormin Huang)
Abstract	In recent years, with the improvement of sensor technologies, the volumes of remote sensing data are increased dramatically. The feature extraction of hyper spectral remotely sensed images can reduce such high-dimensional datasets, solve the big data problem, avoid the Hughes phenomena and improve the classification performance. Accordingly, this paper presents a framework for feature extraction of hyper spectral imagery, which consists of two approaches, referred to as parallel particle swarm optimization (PPSO) band selection and weighted voting impurity function (WVIF) band prioritization. The highly correlated bands of hyper spectral imagery can be grouped first into the some modules by PPSO band selection algorithm to coarsely reduce high-dimensional datasets, and these highly correlated band modules can then be analyzed with the statistical relationship between bands and classes by WVIF band prioritization method to finely select the most important feature bands form the datasets. Furthermore, a PPSO algorithm based on modern graphics processing unit (GPU) architecture using NVIDIA compute unified device architecture (CUDA) technology is using in this paper. It can improve the computational speed of PPSO band selection to group the high correlated band modules. The effectiveness of the proposed PPSO/WVIF framework is evaluated by MASTER and AVIRIS hyper spectral images. The experimental results demonstrated that the proposed method not only could reduction the dimension of datasets, but also can offer a satisfactory classification performance and computational speed.
Sponsorship	IEEE Tech. Comm. Distrib. Process.
Starting Page	699
Ending Page	703
File Size	1110010
Page Count	5
File Format	PDF
ISBN	9781479920815
ISSN	15219097
DOI	10.1109/ICPADS.2013.124
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-15
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Feature extraction Graphics processing units Training Particle swarm optimization Impurities graphics processing unit hyperspectral images particle swarm optimization band selection weighted voting impurity function band prioritization
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture

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