Prof. Peijun Du Profile

Prof. Peijun Du

Professor at Nanjing Univ/School of Geography

SPIE Involvement:

Author

SPIE Journal Paper | 13 October 2018

Weighted multiscale region-level sparse representation for classification of high-spatial resolution remote sensing images

Sensen Chu, Liang Hong, Liang Cheng, Peijun Du, Wenwen Zong

JARS, Vol. 12, Issue 04, 045005, (October 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.045005

KEYWORDS: Image segmentation, Image classification, Image processing algorithms and systems, Remote sensing, Image resolution, Feature extraction, Image processing, Roads, Chemical species, Associative arrays

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SPIE Journal Paper | 21 January 2015

Monitoring urban impervious surface area change using China-Brazil Earth Resources Satellites and HJ-1 remote sensing images

Peijun Du, Junshi Xia, Li Feng

JARS, Vol. 9, Issue 01, 096094, (January 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096094

KEYWORDS: Remote sensing, Satellites, Spectral models, Spatial resolution, Satellite imaging, Vegetation, Data modeling, Multispectral imaging, Image classification, Earth observing sensors

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SPIE Journal Paper | 22 August 2014

Dynamic classifier selection using spectral-spatial information for hyperspectral image classification

Hongjun Su, Bin Yong, Peijun Du, Hao Liu, Chen Chen, Kui Liu

JARS, Vol. 8, Issue 01, 085095, (August 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.085095

KEYWORDS: Image classification, Hyperspectral imaging, Feature extraction, 3D modeling, Statistical analysis, Principal component analysis, Associative arrays, Remote sensing, Classification systems, Roads

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SPIE Journal Paper | 8 August 2014

Modified multiple endmember spectral mixture analysis for mapping impervious surfaces in urban environments

Kun Tan, Xiao Jin, Qian Du, Peijun Du

JARS, Vol. 8, Issue 01, 085096, (August 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.085096

KEYWORDS: Hyperspectral imaging, Vegetation, Reflectivity, Associative arrays, Sensors, Accuracy assessment, Data acquisition, Signal to noise ratio, Error analysis, Remote sensing

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SPIE Journal Paper | 17 July 2012

Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery

Hongjun Su, Peijun Du, Qian Du

OE, Vol. 51, Issue 11, 111715, (July 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.11.111715

KEYWORDS: Hyperspectral imaging, Optical engineering, Data analysis, Roads, Remote sensing, Principal component analysis, Absorption, Information science, Distance measurement, Data storage

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Proceedings Article | 7 November 2008 Paper

Incorporating spectral, texture, and shape information for high spatial resolution satellite imagery classification

Yindi Zhao, Peijun Du

Proceedings Volume 7147, 714719 (2008) https://doi.org/10.1117/12.813246

KEYWORDS: Image classification, Spatial resolution, Feature extraction, High resolution satellite images, Earth observing sensors, Satellites, Vegetation, Shape analysis, Radon, Near infrared

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Proceedings Article | 5 November 2008 Paper

Urban traffic safety analysis and assessment system based on GIS: system design, key techniques and implementation strategy

Peijun Du, Jianzhong Pei, Xiaowei Chen, Zhigang Yan

Proceedings Volume 7144, 71442U (2008) https://doi.org/10.1117/12.812852

KEYWORDS: Safety, Geographic information systems, Roads, Databases, Data modeling, Analytical research, Data storage, Data backup, Data communications, Visualization

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Proceedings Article | 8 August 2007 Paper

Spectral features recognition based on data mining algorithms

Peijun Du, Hongjun Su, Wei Zhang

Proceedings Volume 6752, 675204 (2007) https://doi.org/10.1117/12.760106

KEYWORDS: Remote sensing, Data mining, Feature extraction, Detection and tracking algorithms, Reflectivity, Absorption, Algorithm development, Data processing, Target recognition, Hyperspectral imaging

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Proceedings Article | 8 August 2007 Paper

Wavelet-based feature extraction and similarity measure in hyperspectral remote sensing

Wei Zhang, Peijun Du

Proceedings Volume 6752, 67520D (2007) https://doi.org/10.1117/12.760484

KEYWORDS: Wavelets, Absorption, Remote sensing, Feature extraction, Quantitative analysis, Chemical analysis, Spectroscopy, Target recognition, Object recognition, Image classification

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Proceedings Article | 26 July 2007 Paper

Pattern change and dynamic evolution of urban green space based on multi-temporal remote sensing images: a case study of Xuzhou City

Huapeng Zhang, Peijun Du, Chen Pan, Zuoxia Yin

Proceedings Volume 6752, 67522P (2007) https://doi.org/10.1117/12.760788

KEYWORDS: Remote sensing, Mining, Vegetation, Agriculture, Statistical analysis, Principal component analysis, Landsat, Analytical research, Image classification, Earth observing sensors

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Proceedings Article | 28 October 2006 Paper

Some new classification methods for hyperspectral remote sensing

Pei-jun Du, Yun-hao Chen, Simon Jones, Jelle Ferwerda, Zhi-jun Chen, Hua-peng Zhang, Kun Tan, Zuo-xia Yin

Proceedings Volume 6419, 641929 (2006) https://doi.org/10.1117/12.713419

KEYWORDS: Image classification, Remote sensing, Data fusion, Information fusion, Image fusion, Image segmentation, Hyperspectral imaging, Neural networks, Image processing, Principal component analysis

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Proceedings Article | 28 October 2006 Paper

A Web-based open-source database for the distribution of hyperspectral signatures

J. Ferwerda, S. Jones, Pei-Jun Du

Proceedings Volume 6421, 64210G (2006) https://doi.org/10.1117/12.712664

KEYWORDS: Databases, Data storage, Binary data, Computer security, Field spectroscopy, Reflectivity, Sensors, Nitrogen, Data acquisition, Computer programming

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Proceedings Article | 19 May 2006 Paper

Hyperspectral remote sensing image retrieval based on spectral similarity measure

Peijun Du, Yunhao Chen, Hong Tang, Tao Fang

Proceedings Volume 6199, 61990I (2006) https://doi.org/10.1117/12.673667

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Spectral similarity measure plays important roles in hyperspectral Remote Sensing (RS) information processing, and it can be used to content-based hyperspectral RSimage retrieval effectively too. The applications of spectral features to Remote Sensing (RS) image retrieval are discussed by taking hyperspectral RS image as examples oriented to the demands of massive information management. It is proposed that spectral features-based image retrieval includes two modes: retrieval based on point template and facial template. Point template is used usually, for example, a spectral curve, or a pixel vector in hyperspectral RS image. One or more regions (or blocks with area shape) are given as examples in image retrieval based on facial template. The most important issues in image retrieval are spectral features extraction and spectral similarity measure. Spectral vector can be used to retrieval directly, and spectral angle and spectral information divergence (SID) are more effective than Euclidean distance and correlation coefficient in similarity measure and image retrieval. Both point and pure area template can be transformed into spectral vector and used to spectral similarity measure. In addition, the local maximum and minimum in reflection spectral curve, corresponding to reflection peak and absorption valley, can be used to retrieval also. The width, height, symmetry and power of each peak or valley can be used to encode spectral features. By comparison to three approaches for spectral absorption and reflection features matching and similarity measures, it is found that spectral absorption and reflection features are not very effective in hyperspectral RS image retrieval. Finally, a prototype system is designed, and it proves that the hyperspectral RS image retrieval based on spectral similarity measure proposed in this paper is effective and some similarity measure index including spectral angle, SID and encoding measure are suitable for image retrieval in practice.

Proceedings Article | 4 November 2005 Paper

On the filtering of hyperspectral remote sensing image

Peijun Du, Yunhao Chen, Yonggui Yang, Huapeng Zhang

Proceedings Volume 6044, 604418 (2005) https://doi.org/10.1117/12.655105

KEYWORDS: Image filtering, Optical filters, Digital filtering, Remote sensing, Image quality, Hyperspectral imaging, Image enhancement, Image processing, 3D image processing, Data processing

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Proceedings Article | 31 July 2002 Paper

Information characteristics and band combination of Landsat TM RS image used to terrestrial surface evolution in mining area

Pei-jun DU, Tao Fang, Da-zhi GUO, Pengfei Shi

Proceedings Volume 4875, (2002) https://doi.org/10.1117/12.477154

KEYWORDS: Mining, Remote sensing, Landsat, Genetic algorithms, Statistical analysis, Environmental monitoring, Satellite imaging, Satellites, Principal component analysis, Earth observing sensors

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Showing 5 of 15 publications

Conference Committee Involvement (13)

Image and Signal Processing for Remote Sensing XXIX

4 September 2023 | Amsterdam, Netherlands

Image and Signal Processing for Remote Sensing XXVIII

5 September 2022 | Berlin, Germany

Image and Signal Processing for Remote Sensing XXVII

13 September 2021 | Online Only, Spain

Image and Signal Processing for Remote Sensing XXVI

21 September 2020 | Online Only, United Kingdom

Image and Signal Processing for Remote Sensing XXV

9 September 2019 | Strasbourg, France

Showing 5 of 13 Conference Committees

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