Dr. Wei Li Profile

Dr. Wei Li

Research Associate at Beijing Univ of Chemical Technology

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Author

Publications (23)

Proceedings Article | 27 January 2021 Paper

Infrared small target detection in image sequences based on temporal low-rank and sparse decomposition

Yan Nie, Wei Li, Mingjing Zhao, Qiong Ran, Pengge Ma

Proceedings Volume 11720, 117200A (2021) https://doi.org/10.1117/12.2589426

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Proceedings Article | 24 October 2017 Paper

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

Lan Chang, Mengmeng Zhang, Wei Li

Proceedings Volume 10461, 104610J (2017) https://doi.org/10.1117/12.2283229

KEYWORDS: Medical imaging, Hyperspectral imaging, Image classification, Image segmentation

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Proceedings Article | 24 October 2017 Paper

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

Qiong Ran, Lan Chang, Wei Li, Xiaofeng Xu

Proceedings Volume 10461, 1046102 (2017) https://doi.org/10.1117/12.2281268

KEYWORDS: Image classification, Hyperspectral imaging, Medical image processing

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Proceedings Article | 5 October 2017 Presentation + Paper

Low-complexity multiple collaborative representations for hyperspectral image classification

Yan Xu, Qian Du, Wei Li, Nicolas Younan

Proceedings Volume 10430, 1043003 (2017) https://doi.org/10.1117/12.2278841

KEYWORDS: Hyperspectral imaging, Image classification, Parallel computing, Algorithm development

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SPIE Journal Paper | 1 September 2017

Collaborative classification of hyperspectral and visible images with convolutional neural network

Mengmeng Zhang, Wei Li, Qian Du

JARS, Vol. 11, Issue 04, 042607, (September 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.042607

KEYWORDS: Image classification, Image segmentation, Image fusion, Hyperspectral imaging, Spatial resolution, Lithium, Data fusion, Information fusion, Convolutional neural networks, Remote sensing

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SPIE Journal Paper | 12 January 2017

Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery

Haitao Lang, Xingyao Zhang, Yuyang Xi, Xi Zhang, Wei Li

JARS, Vol. 11, Issue 01, 015006, (January 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.015006

KEYWORDS: Image fusion, Image segmentation, Synthetic aperture radar, Feature extraction, Binary data, Image classification, Image processing algorithms and systems, Image filtering, Neurons, Digital filtering

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In recent years, oil spill surveillance with space-borne synthetic aperture radar (SAR) has received unprecedented attention and has been gradually developed into a common technique for maritime environment protection. A typical SAR-based oil spill detection process consists of three steps: (1) dark-spot segmentation, (2) feature extraction, and (3) oil spill and look-alike discrimination. As a preliminary task in the oil spill detection process chain, dark-spot segmentation is a critical and fundamental step prior to feature extraction and classification, since its output has a direct impact on the two subsequent stages. The balance between the detection probability and false alarm probability has a vital impact on the performance of the entire detection system. Unfortunately, this problem has not drawn as much attention as the other two stages. A specific effort has been placed on dark-spot segmentation in single-pol SAR imagery. A combination of fine designed features, including gray features, geometric features, and textural features, is proposed to characterize the oil spill and seawater for improving the performance of dark-spot segmentation. In the proposed process chain, a histogram stretching transform is incorporated before the gray feature extraction to enhance the contrast between possible oil spills and water. A simple but effective multiple-level thresholding algorithm is developed to conduct a binary classification before the geometric feature extraction to obtain more accurate area features. A local binary pattern code is computed and assigned as the textural feature for a pixel to characterize the physical difference between oil spills and water. The experimental result confirms that the proposed fine designed feature combination outperforms existing approaches in both aspects of overall segmentation accuracy and the capability to balance detection probability and false alarm probability. It is a promising alternative that can be incorporated into existing oil spill detection systems to further improve system performance.

SPIE Journal Paper | 6 September 2016

Change detection with one-class sparse representation classifier

Qiong Ran, Mengmeng Zhang, Wei Li, Qian Du

JARS, Vol. 10, Issue 04, 042006, (September 2016) https://doi.org/10.1117/12.10.1117/1.JRS.10.042006

KEYWORDS: Floods, Lithium, Error analysis, Satellites, Information technology, Multispectral imaging, Algorithm development, Analytical research, Vegetation, Image classification

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Proceedings Article | 17 May 2016 Paper

A generalized representation-based approach for hyperspectral image classification

Jiaojiao Li, Wei Li, Qian Du, Yunsong Li

Proceedings Volume 9840, 984021 (2016) https://doi.org/10.1117/12.2224494

KEYWORDS: Hyperspectral imaging, Image classification, Associative arrays, Chemical species, Error analysis, Machine learning, Error control coding, Lithium, Algorithm development, Library classification systems

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Proceedings Article | 9 December 2015 Paper

Sparse representation and smooth filtering for hyperspectral image classification

Mengmeng Zhang, Qiong Ran, Wei Li, Kui Liu

Proceedings Volume 9808, 98083P (2015) https://doi.org/10.1117/12.2205325

KEYWORDS: Image filtering, Hyperspectral imaging, Image classification, Reflectivity, Optical filters, Error analysis, Algorithm development, Image analysis, Feature extraction, Spatial filters

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Proceedings Article | 9 December 2015 Paper

Locality-constrained anomaly detection for hyperspectral imagery

Jiabin Liu, Wei Li, Qian Du, Kui Liu

Proceedings Volume 9808, 980803 (2015) https://doi.org/10.1117/12.2205326

KEYWORDS: Sensors, Hyperspectral imaging, Detection and tracking algorithms, Hyperspectral target detection, Target detection, Mahalanobis distance, Statistical analysis, Image fusion, Algorithm development, Associative arrays

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SPIE Journal Paper | 14 July 2015

Three-dimensional terrain model multiview quality assessment considering human visual system

Fan Zhang, Xiaoyang Li, Huan Liu, Wei Li, Wei Hu, Hengchao Li

JARS, Vol. 9, Issue 01, 097290, (July 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.097290

KEYWORDS: 3D modeling, Data modeling, Visualization, Visual process modeling, 3D image processing, Image quality, Performance modeling, Visual system, Wavelets, Statistical modeling

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Proceedings Article | 21 May 2015 Paper

Improving the performance of extreme learning machine for hyperspectral image classification

Jiaojiao Li, Qian Du, Wei Li, Yunsong Li

Proceedings Volume 9501, 950109 (2015) https://doi.org/10.1117/12.2178013

KEYWORDS: Neurons, Hyperspectral imaging, Image classification, Neural networks, Statistical analysis, Sensors, Telecommunications, Computer engineering, Standards development, Target detection

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Proceedings Article | 21 May 2015 Paper

Support vector machine with adaptive composite kernel for hyperspectral image classification

Wei Li, Qian Du

Proceedings Volume 9501, 95010O (2015) https://doi.org/10.1117/12.2178012

KEYWORDS: Image classification, Digital filtering, Gaussian filters, Hyperspectral imaging, Composites, Spatial filters, Spatial resolution, Feature extraction, Image filtering, Neurons

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Proceedings Article | 21 May 2015 Paper

Kernel weighted joint collaborative representation for hyperspectral image classification

Qian Du, Wei Li

Proceedings Volume 9501, 95010V (2015) https://doi.org/10.1117/12.2179914

KEYWORDS: Error control coding, Chemical species, Hyperspectral imaging, Image classification, Associative arrays, Composites, Library classification systems, Sensors, Roads, Laser induced breakdown spectroscopy

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SPIE Journal Paper | 6 March 2015

Hyperspectral image classification for mapping agricultural tillage practices

Qiong Ran, Wei Li, Qian Du, Chenghai Yang

JARS, Vol. 9, Issue 01, 097298, (March 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.097298

KEYWORDS: Agriculture, Image classification, Hyperspectral imaging, Remote sensing, Laser range finders, Associative arrays, Image filtering, Linear filtering, Digital filtering, Image processing

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SPIE Journal Paper | 2 March 2015

Optimizing extreme learning machine for hyperspectral image classification

Jiaojiao Li, Qian Du, Wei Li, Yunsong Li

JARS, Vol. 9, Issue 01, 097296, (March 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.097296

KEYWORDS: Neurons, Image classification, Hyperspectral imaging, Neural networks, Lithium, Machine learning, Remote sensing, Statistical analysis, Information technology, Spectral resolution

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SPIE Journal Paper | 26 February 2015 Open Access

Decision fusion for dual-window-based hyperspectral anomaly detector

Wei Li, Qian Du

JARS, Vol. 9, Issue 01, 097297, (February 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.097297

KEYWORDS: Sensors, Target detection, Hyperspectral target detection, Image fusion, Detection and tracking algorithms, Hyperspectral imaging, Data fusion, Lithium, Parallel computing, Data modeling

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Proceedings Article | 18 November 2014 Paper

Study on mosaic method for new mode satellite images with high spatial resolution covering urban areas

Qiong Ran, Zhiyong Wang, Qiang Wen, Wei Li, Lianru Gao

Proceedings Volume 9263, 926305 (2014) https://doi.org/10.1117/12.2065536

KEYWORDS: Roads, Satellites, Spatial resolution, Satellite imaging, Buildings, Earth observing sensors, Image processing, High resolution satellite images, Cameras, Remote sensing

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SPIE Journal Paper | 27 October 2014

Hyperspectral image classification with improved local-region filters

Qiong Ran, Wei Li, Qian Du, Mingming Xiong

JARS, Vol. 8, Issue 01, 085088, (October 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.085088

KEYWORDS: Image filtering, Image classification, Digital filtering, Hyperspectral imaging, Optical filters, Distance measurement, Laser range finders, Image processing, Feature extraction, Spatial resolution

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SPIE Journal Paper | 28 July 2014

Representation-based classifications with Markov random field model for hyperspectral urban data

Mingming Xiong, Fan Zhang, Qiong Ran, Wei Hu, Wei Li

JARS, Vol. 8, Issue 01, 085097, (July 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.085097

KEYWORDS: Magnetorheological finishing, Data modeling, Image classification, Hyperspectral imaging, Visual process modeling, Image analysis, Error control coding, Reflectivity, Optical systems, Imaging systems

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SPIE Journal Paper | 18 July 2014

Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit

Wei Li, Xin Huang, Qian Du, Jinyi Zou

JARS, Vol. 8, Issue 01, 085099, (July 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.085099

KEYWORDS: Image segmentation, Image classification, Associative arrays, Data centers, Hyperspectral imaging, Lithium, Remote sensing, Statistical modeling, Error analysis, Metals

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Proceedings Article | 22 May 2014 Paper

An efficient spatial-spectral classification method for hyperspectral imagery

Wei Li, Qian Du

Proceedings Volume 9124, 912410 (2014) https://doi.org/10.1117/12.2050710

KEYWORDS: Hyperspectral imaging, Image classification, Feature extraction, Image filtering, Optical filters, Yield improvement, 3D image processing, Hyperspectral simulation, Remote sensing, Algorithm development

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SPIE Journal Paper | 17 March 2014

Wavelet-based nearest-regularized subspace for noise-robust hyperspectral image classification

Wei Li, Kui Liu, Hongjun Su

JARS, Vol. 8, Issue 01, 083665, (March 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083665

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Showing 5 of 23 publications

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