Dr. Yuanfeng Wu Profile

Dr. Yuanfeng Wu

Associate Professor at Institute of Remote Sensing and Digital Earth

SPIE Involvement:

Author

Proceedings Article | 5 October 2017 Paper

GPU implementation of discrete particle swarm optimization algorithm for endmember extraction from hyperspectral image

Chaoyin Yu, Zhengwu Yuan, Yuanfeng Wu

Proceedings Volume 10430, 104300G (2017) https://doi.org/10.1117/12.2279983

KEYWORDS: Particles, Particle swarm optimization, Hyperspectral imaging, Image analysis, Probability theory, Feature extraction, Optimization (mathematics), Image processing, Data analysis, Computer simulations

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Proceedings Article | 20 October 2015 Paper

Embedded GPU implementation of anomaly detection for hyperspectral images

Yuanfeng Wu, Lianru Gao, Bing Zhang, Bin Yang, Zhengchao Chen

Proceedings Volume 9646, 964608 (2015) https://doi.org/10.1117/12.2195460

KEYWORDS: Hyperspectral imaging, Algorithm development, Detection and tracking algorithms, Target detection, Graphics processing units, Hyperspectral target detection, Sensors, Statistical analysis, Computer simulations, Earth sciences

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Proceedings Article | 20 October 2015 Paper

Fault tolerance of SVM algorithm for hyperspectral image

Yabo Cui, Zhengwu Yuan, Yuanfeng Wu, Lianru Gao, Hao Zhang

Proceedings Volume 9646, 964610 (2015) https://doi.org/10.1117/12.2196704

KEYWORDS: Tolerancing, Hyperspectral imaging, Image classification, Image analysis, Image processing, Detection and tracking algorithms, Binary data, Error analysis, Digital image processing, Remote sensing

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SPIE Journal Paper | 30 March 2015

Graphics processing unit–accelerated computation of the Markov random fields and loopy belief propagation algorithms for hyperspectral image classification

Yuanfeng Wu, Jun Li, Lianru Gao, Xuemin Tan, Bing Zhang

JARS, Vol. 9, Issue 01, 097295, (March 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.097295

KEYWORDS: Hyperspectral imaging, Image classification, Graphics processing units, Spectroscopy, Image processing, Linear algebra, Imaging systems, Optimization (mathematics), Data modeling, Library classification systems

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We propose a commodity graphics processing units (GPUs)–based massively parallel efficient computation for spectral-spatial classification of hyperspectral images. The spectral-spatial classification framework is based on the marginal probability distribution which uses all of the information in the hyperspectral data. In this framework, first, the posterior class probability is modeled with discriminative random field in which the association potential is linked with a multinomial logistic regression (MLR) classifier and the interaction potential modeling the spatial information is linked to a Markov random field multilevel logistic (MLL) prior. Second, the maximizers of the posterior marginals are computed via the loopy belief propagation (LBP) method. In addition, the regressors of the multinominal logistic regression classifier are inferred by the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. Although the spectral-spatial classification framework exhibited state-of-the-art accuracy performance with regard to similar approaches, its computational complexity is very high. We take advantage of the massively parallel computing capability of NVIDIA Tesla C2075 with the compute unified device architecture including a set of GPU-accelerated linear algebra libraries (CULA) to dramatically improve the computation speed of this hyperspectral image classification framework. The shared memory and the asynchronous transfer techniques are also used for further computationally efficient optimization. Real hyperspectral data sets collected by the National Aeronautics and Space Administration’s airborne visible infrared imaging spectrometer and the reflective optics system imaging spectrometer system are used for effectiveness evaluation. The results show that we achieved a speedup of 92-fold on LORSAL, 69-fold on MLR, 127-fold on MLL, 160-fold on LBP, and 73-fold on the whole spectral-spatial classification framework as compared with its single-core central processing unit counterpart, respectively.

SPIE Journal Paper | 13 August 2014

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

Yuanfeng Wu, Lianru Gao, Bing Zhang, Haina Zhao, Jun Li

JARS, Vol. 8, Issue 01, 084797, (August 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.084797

KEYWORDS: Hyperspectral imaging, Feature extraction, Data transmission, Principal component analysis, Signal to noise ratio, Interference (communication), 3D modeling, Graphics processing units, Image classification, Computer architecture

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Proceedings Article | 23 May 2013 Paper

Optimising the use of hyperspectral and multispectral data for regional crop classification

Li Ni, Bing Zhang, Lianru Gao, Shanshan Li, Yuanfeng Wu

Proceedings Volume 8745, 87451V (2013) https://doi.org/10.1117/12.2015646

KEYWORDS: Associative arrays, Hyperspectral simulation, Image classification, Data centers, Spectral resolution, Data conversion, Accuracy assessment, Hyperspectral imaging, Signal to noise ratio, Agriculture

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Proceedings Article | 18 May 2013 Paper

GPUs for parallel on-board hyperspectral image radiometric normalization

Yuanfeng Wu, Bing Zhang, Haina Zhao, Jianwei Gao, Li Ni, Wei Yang

Proceedings Volume 8743, 874322 (2013) https://doi.org/10.1117/12.2015561

KEYWORDS: Sensors, Hyperspectral imaging, Data processing, Image sensors, Computer architecture, Image processing, Electroluminescence, Graphics processing units, Digital image processing, Image analysis

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Proceedings Article | 24 May 2012 Paper

DSP design for real-time hyperspectral target detection based on spatial-spectral information extraction

Wei Yang, Bing Zhang, Lianru Gao, Yuanfeng Wu

Proceedings Volume 8390, 83902M (2012) https://doi.org/10.1117/12.918386

KEYWORDS: Digital signal processing, Image processing, Detection and tracking algorithms, Hyperspectral imaging, Target detection, Signal processing, Hyperspectral target detection, Remote sensing, Computer architecture, Target recognition

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Proceedings Article | 30 October 2009 Paper

The study and implementation of extraction HJ1A HSI image data based on A HDF5 file

Yuanfeng Wu, Bing Zhang, Junsheng Li, Hao Zhang, Qian Shen, Di Wu

Proceedings Volume 7494, 74941L (2009) https://doi.org/10.1117/12.833065

KEYWORDS: Data modeling, Feature extraction, Satellites, RGB color model, Calibration, Image quality, Satellite imaging, Data storage, Spectroscopy, Image sensors

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Showing 5 of 9 publications

Conference Committee Involvement (3)

High-Performance Computing in Geoscience and Remote Sensing

13 September 2018 | Berlin, Germany

High-Performance Computing in Geoscience and Remote Sensing

12 September 2017 | Warsaw, Poland

High-Performance Computing in Geoscience and Remote Sensing

28 September 2016 | Edinburgh, United Kingdom

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