Dr. Ying Zhang Profile

Dr. Ying Zhang

Research Scientist at Natural Resources Canada

SPIE Involvement:

Conference Chair | Author | Editor

Proceedings Article | 1 November 2024 Presentation + Paper

Post-wildfire vegetation cover change mapping for assessments of secondary disaster risks toward urban areas: case of Fort McMurray 2016

Ying Zhang, Sylvain Leblanc, Francis Canisius, Haoyu Fang, Maxim Fortin, Julie Lovitt

Proceedings Volume 13198, 1319802 (2024) https://doi.org/10.1117/12.3027009

KEYWORDS: Vegetation, Landsat, Land cover, Earth observing sensors, Risk assessment, Image resolution, Image fusion, Floods

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Proceedings Article | 12 September 2021 Presentation

Optical remote sensing for urban flood applications: Canadian case studies

Ying Zhang

Proceedings Volume 11864, 118640I (2021) https://doi.org/10.1117/12.2599630

KEYWORDS: Floods, Remote sensing, Associative arrays, Data modeling, Optical resolution, Climatology, Climate change

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Proceedings Article | 20 September 2020 Presentation

Opening Remarks by Conference Chairs

Thilo Erbertseder, Ying Zhang, Nektarios Chrysoulakis

Proceedings Volume 11535, 1153502 (2020) https://doi.org/10.1117/12.2584139

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Proceedings Article | 20 September 2020 Presentation

Object-based Automated Mapping of Floodwater in Dense Urban Areas

Ying Zhang

Proceedings Volume 11535, 1153508 (2020) https://doi.org/10.1117/12.2573273

KEYWORDS: RGB color model, Floods, Remote sensing, Image segmentation, Sensors, Feature extraction

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SPIE Journal Paper | 23 November 2019

Effectiveness of aerial and ISERV-ISS RGB photos for real-time urban floodwater mapping: case of Calgary 2013 flood

Ying Zhang, Francis Canisius, Chuiqing Zhen, Boyu Feng, Peter Crawford, Lucia Huang

JARS, Vol. 13, Issue 04, 044521, (November 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.044521

KEYWORDS: RGB color model, Floods, Sensors, Spatial resolution, Buildings, Data acquisition, Unmanned aerial vehicles, Associative arrays, Roads, Visible radiation

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Proceedings Article | 2 October 2019 Presentation + Paper

Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data

Ying Zhang, Sylvian Leblanc

Proceedings Volume 11157, 111570F (2019) https://doi.org/10.1117/12.2527767

KEYWORDS: Buildings, LIDAR, Earthquakes, Associative arrays, Multispectral imaging, Spatial resolution, Satellites, Image resolution, Remote sensing, Sensors

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Proceedings Article | 9 October 2018 Paper

Effectiveness of RGB imagery from diverse sources for real-time urban flood water mapping

Ying Zhang, Francis Canisius, Bert Guindon, Chuiqing Zhen, Boyu Feng, Jinfei Wang

Proceedings Volume 10793, 107930E (2018) https://doi.org/10.1117/12.2323495

KEYWORDS: Floods, RGB color model, Spatial resolution, Sensors, Remote sensing

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SPIE Journal Paper | 24 March 2017

Spectral-analysis-based extraction of land disturbances arising from oil and gas development in diverse landscapes

Ying Zhang, Nicholas Lantz, Bert Guindon, Xianfeng Jiao

JARS, Vol. 11, Issue 01, 015026, (March 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.015026

KEYWORDS: Image segmentation, Roads, Agriculture, Satellites, Mining, Near infrared, Satellite imaging, Image classification, Earth observing sensors, Vegetation

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Proceedings Article | 26 October 2016 Presentation + Paper

Effective delineation of urban flooded areas based on aerial ortho-photo imagery

Ying Zhang, Bert Guindon, Don Raymond, Gang Hong

Proceedings Volume 10008, 100080M (2016) https://doi.org/10.1117/12.2241128

KEYWORDS: Floods, Associative arrays, Data modeling, Remote sensing, Climate change, Synthetic aperture radar, Geographic information systems, Buildings, Photography, Satellites

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SPIE Journal Paper | 13 January 2014

Target-driven extraction of built-up land changes from high-resolution imagery

Ying Zhang, Bert Guindon, Xinwu Li, Nicholas Lantz, Zhongchang Sun

JARS, Vol. 8, Issue 01, 084594, (January 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.084594

KEYWORDS: Image segmentation, Spatial resolution, Image classification, Vegetation, Roads, Sensors, Information fusion, Remote sensing, Agriculture, Image resolution

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SPIE Journal Paper | 20 November 2013 Open Access

Spatiotemporal analysis of urban environment based on the vegetation–impervious surface–soil model

Huadong Guo, Qingni Huang, Xinwu Li, Zhongchang Sun, Ying Zhang

JARS, Vol. 8, Issue 01, 084597, (November 2013) https://doi.org/10.1117/12.10.1117/1.JRS.8.084597

KEYWORDS: Vegetation, Environmental sensing, Data modeling, Soil science, Landsat, Remote sensing, Error analysis, Image classification, Satellites, Earth observing sensors

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Proceedings Article | 24 October 2013 Paper

Quantification of changes in oil sands mining infrastructure land based on RapidEye and SPOT5

Ying Zhang, Nicholas Lantz, Bert Guindon, Todd Shipman, Dennis Chao, Don Raymond

Proceedings Volume 8893, 889302 (2013) https://doi.org/10.1117/12.2029001

KEYWORDS: Mining, Image segmentation, Image resolution, Vegetation, Sensors, Spatial resolution, Roads, Satellites, Accuracy assessment, Earth observing sensors

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Proceedings Article | 27 August 2008 Paper

Improved iterative error analysis for endmember extraction from hyperspectral imagery

Lixin Sun, Ying Zhang, Bert Guindon

Proceedings Volume 7086, 70860S (2008) https://doi.org/10.1117/12.799232

KEYWORDS: Hyperspectral imaging, Feature extraction, Image processing, Error analysis, Remote sensing, Algorithm development, Shape memory alloys, Spectroscopy, Evolutionary algorithms, Image quality

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Automated image endmember extraction from hyperspectral imagery is a challenge and a critical step in spectral mixture analysis (SMA). Over the past years, great efforts were made and a large number of algorithms have been proposed to address this issue. Iterative error analysis (IEA) is one of the well-known existing endmember extraction methods. IEA identifies pixel spectra as a number of image endmembers by an iterative process. In each of the iterations, a fully constrained (abundance nonnegativity and abundance sum-to-one constraints) spectral unmixing based on previously identified endmembers is performed to model all image pixels. The pixel spectrum with the largest residual error is then selected as a new image endmember. This paper proposes an updated version of IEA by making improvements on three aspects of the method. First, fully constrained spectral unmixing is replaced by a weakly constrained (abundance nonnegativity and abundance sum-less-or-equal-to-one constraints) alternative. This is necessary due to the fact that only a subset of endmembers exhibit in a hyperspectral image have been extracted up to an intermediate iteration and the abundance sum-to-one constraint is invalid at the moment. Second, the search strategy for achieving an optimal set of image endmembers is changed from sequential forward selection (SFS) to sequential forward floating selection (SFFS) to reduce the so-called "nesting effect" in resultant set of endmembers. Third, a pixel spectrum is identified as a new image endmember depending on both its spectral extremity in the feature hyperspace of a dataset and its capacity to characterize other mixed pixels. This is achieved by evaluating a set of extracted endmembers using a criterion function, which is consisted of the mean and standard deviation of residual error image. Preliminary comparison between the image endmembers extracted using improved and original IEA are conducted based on an airborne visible infrared imaging spectrometer (AVIRIS) dataset acquired over Cuprite mining district, Nevada, USA.