I recieved my PhD degree in Geography in 2014 from Department of Geography and Environment Management, University of Waterloo. I achieved my Master in 2010 and Bachelor degrees in 2007, both in geomatics engineering, from China University of Geosciences (Beijing). 

In my key research areas, I have demonstrated excellence, leadership and recognition. I, as principal investigator, have successfully secured many competitive grants, including NSERC Discovery Grant. I have published one book chapter, 66 journal papers on high-rank remote sensing journals and 41 conference articles on high-impact conferences.

I have been involved in many invited talks and presentations to different audiences, and I frequently receive reviewer invitations from high-impact remote sensing journals and conferences. I serve as the Associate Editor of Frontiers in Environmental Science (Section: Big Data, AI and the Environment).

LinkedIn: linkedin.com/linlinxu.

Google Scholar: https://scholar.google.com/linlinxu

Email: l44xu@uwaterloo.ca

Students

Supervision – Current

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Ph.D

M.A.Sc

Supervision – Completed

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Ph.D

M.A.Sc

Email: p5yu@alumni.uwaterloo.ca

Website: http://www.peteryu.ca

Her research is primarily in image and video processing, statistical modeling and machine learning. She has published and reviewed several papers for key journals and conferences in the related areas of image processing, image analysis and pattern recognition.

Before her PhD, Ying worked for four years on the research and development of signal processing for underwater systems. In 2010, she collaborated with the compression research group in Research In Motion Ltd. on video processing research for the coming standard HEVC. Recently, she is collaborating with Center of Pattern Analysis and Machine Intelligence, University of Waterloo and working on data analysis and machine learning.

Email: 30liu@engmail.uwaterloo.ca

Most previous methods are designed to generate textural characteristics only and cannot provide a corresponding label field. Moreover, in many situations, those texture methods are ill-suited and do not capture the complex structural characteristics found in remote sensing data.

In this work, we address the above issues by decoupling the synthesis of texture (Fig. 1) and label field (Fig. 2) into two different modeling structures. That is, we will explicitly synthesize the discrete-state label field (Fig. 3), which contains the complex structural characteristics of the remote sensing image, and separately synthesize the textures (Fig.4) of remote sensing images using a modification of the nonparametric texture synthesis strategy.

Given the scale-dependent, non-stationary nature of remotely sensed data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex discrete-state structures in a realistic manner. (Fig. 5)

 Synthesis results using operational RADARSAT-1 SAR sea-ice imagery provided by the Canadian Ice Service (CIS) show that the proposed approach can well model complex nonstationary scale structures, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.

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Conference Papers

Leigh, S., Z. Wang, and D. A. Clausi, “Evaluation of MAGIC Sea Ice Classifier on 61 Dual Polarization RADARSAT-2 Scenes”, IEEE Geoscience and Remote Sensing Symposium, Quebec City, Quebec, Canada, IEEE Geoscience and Remote Sensing Symposium, 2014. Get it here.

Students

Supervision – Current

PDF

ph.D.

M.A.Sc.

Supervision – Completed

PDF

ph.D.

M.A.Sc.