A Parallel Simulated Annealing Approach to Band Selection for Hyperspectral Imagery

Chang, Yang-Lang; Fang, Jyh-Perng; Liang, Wen-Yew; Chang, Li-Chiu; Ren, Hsuan; Chen, Kun-Shan

doi:10.1109/igarss.2008.4779164

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The parameters obtained by PSA are as the same as reported in Ref. [4]. The parameters used for PPSO are initialized as follows.…”

Section: Resultsmentioning

confidence: 99%

“…Due to the constrain of a single Markov chain (MC) needed to be adjusted in the parallel mechanism, only a limited SA parallelism was exploited [3]. Currently, a new parallel SA method, referred to as a parallel simulated annealing (PSA) band selection approach [4], was introduced to overcome this disadvantage. It can improve the computational speed by using parallel computing techniques.…”

Section: Introductionmentioning

confidence: 99%

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Band selection for hyperspectral images based on parallel particle swarm optimization schemes

Chang

Fang

Benediktsson

et al. 2009

2009 IEEE International Geoscience and Remote Sensing Symposium

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Greedy modular eigenspaces (GME) has been developed for the band selection of hyperspectral images (HSI). GME attempts to greedily select uncorrelated feature sets from HSI. Unfortunately, GME is hard to find the optimal set by greedy operations except by exhaustive iterations. The long execution time has been the major drawback in practice. Accordingly, finding an optimal (or near-optimal) solution is very expensive. In this study we present a novel parallel mechanism, referred to as parallel particle swarm optimization (PPSO) band selection, to overcome this disadvantage. It makes use of a new particle swarm optimization scheme, a well-known method to solve the optimization problems, to develop an effective parallel feature extraction for HSI. The proposed PPSO improves the computational speed by using parallel computing techniques which include the compute unified device architecture (CUDA) of graphics processor unit (GPU), the message passing interface (MPI) and the open multi-processing (OpenMP) applications. These parallel implementations can fully utilize the significant parallelism of proposed PPSO to create a set of near-optimal GME modules on each parallel node. The experimental results demonstrated that PPSO can significantly improve the computational loads and provide a more reliable quality of solution compared to GME. The effectiveness of the proposed PPSO is evaluated by MODIS/ASTER airborne simulator (MASTER) HSI for band selection during the Pacrim II campaign.

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“…The parameters obtained by PSA are as the same as reported in Ref. [4]. The parameters used for PPSO are initialized as follows.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Band selection for hyperspectral images based on parallel particle swarm optimization schemes

Chang

Fang

Benediktsson

et al. 2009

2009 IEEE International Geoscience and Remote Sensing Symposium

Self Cite

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“…This strategy, often referred to as Beowulf-class cluster computing, has already offered accesses to greatly increased computational power, but at a low cost (commensurate with falling commercial PC costs) in several hyperspectral imaging problems [5,6]. In theory, the combination of commercial forces driving down cost and positive hardware trends (e.g., CPU peak power doubling every 18-24 months, storage capacity doubling every 12-18 months, and networking bandwidth doubling every 9-12 months) offers supercomputing performance that can now be applied a much wider range of remote sensing problems [3].…”

Section: Cluster-based Implementationsmentioning

confidence: 99%

High performance computing for hyperspectral image analysis: Perspective and state-of-the-art

Chang

2009

2009 IEEE International Geoscience and Remote Sensing Symposium

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The main purpose of this paper is to describe available (HPC)-based implementations of remotely sensed hyperspectral image processing algorithms on multi-computer clusters, heterogeneous networks of computers, and specialized hardware architectures such as field programmable gate arrays (FPGAs) and graphic processing units (GPUs). Combined, the revision of existing techniques conducted in this paper, along with the description of performance results for a parallel hyperspectral processing chain on different architectures, delivers an excellent snapshot of the state-of-the-art in the area of HPC-based hyperspectral image processing and a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to hyperspectral imaging problems.

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“…Selecting the best subset of bands as representatives always involves searching for the optimum solution, so the use of optimization methods is another important aspect in band selection. A simulated annealing approach was proposed in [19] for hyperspectral band selection and evolutionary computation was applied for this purpose in [20]- [27].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Imagery

Yang¹

Optical Satellite Signal Processing and Enhancement

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In this dissertation, dimensionality reduction for hyperspectral remote sensing imagery is investigated to alleviate practical application difficulties caused by high data dimension. Band selection and band clustering are applied for this purpose. Based on availability of object prior information, supervised, semi-supervised, and unsupervised techniques are proposed. To take advantage of modern computational architecture, parallel implementations on cluster and graphics processing units (GPU) are developed.The impact of dimensionality reduction on the following data analysis is also evaluated.Specific contributions are as below.1. A similarity-based unsupervised band selection algorithm is developed to select distinctive and informative bands, which outperforms other existing unsupervised band selection approaches in the literature.2. An efficient supervised band selection method based on minimum estimated abundance covariance is developed, which outperforms other frequently-used metrics.Template Created By: James Nail 2010This new method does not need to conduct classification during band selection process or examine original bands/band combinations as do traditional approaches.3. An efficient semi-supervised band clustering method is proposed, which uses class signatures to conduct band partition. Compared to traditional unsupervised clustering, computational complexity is significantly reduced.4. Parallel GPU implementations with computational cost saving strategies for the developed algorithms are designed to facilitate onboard processing.5. As an application example, band selection results are used for urban land cover classification. With a few selected bands, classification accuracy can be greatly improved, compared to the one using all the original bands or those from other frequently-used dimensionality reduction methods.ii DEDICATION I would like to dedicate this dissertation to my parents and my brother.iii ACKNOWLEDGMENTS

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A Parallel Simulated Annealing Approach to Band Selection for Hyperspectral Imagery

Cited by 6 publications

References 5 publications

Band selection for hyperspectral images based on parallel particle swarm optimization schemes

Band selection for hyperspectral images based on parallel particle swarm optimization schemes

High performance computing for hyperspectral image analysis: Perspective and state-of-the-art

Dimensionality Reduction of Hyperspectral Imagery

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