Dr. Liang Chen Profile

Dr. Liang Chen

at China Academy of Space Technology

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Author

Publications (5)

SPIE Journal Paper | 6 June 2019 Open Access

Ship detection and tracking method for satellite video based on multiscale saliency and surrounding contrast analysis

Haichao Li, Liang Chen, Feng Li, Meiyu Huang

JARS, Vol. 13, Issue 02, 026511, (June 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.026511

KEYWORDS: Video, Satellites, Video surveillance, Clouds, Image segmentation, Target detection, Detection and tracking algorithms, Satellite imaging, Lithium, Earth observing sensors

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Proceedings Article | 2 November 2018 Paper

Moving target detection based on edge optical flow from satellite videos

Haichao Li, Liang Chen, Feng Li

Proceedings Volume 10817, 108170G (2018) https://doi.org/10.1117/12.2500519

KEYWORDS: Optical flow, Video, Satellites, Video surveillance, Optical tracking, Detection and tracking algorithms, Edge detection, Image registration, Target detection, Cameras

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Proceedings Article | 2 November 2018 Paper

Fast stereo matching using image pyramid for lunar rover

Haichao Li, Feng Li, Liang Chen

Proceedings Volume 10817, 1081703 (2018) https://doi.org/10.1117/12.2500023

KEYWORDS: Image processing, Image analysis, Sensors, Reconstruction algorithms, Visualization, Algorithm development, Cameras

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Proceedings Article | 9 October 2018 Presentation + Paper

Verification of high-resolution and precision TEC retrieval based on the PALSAR full-polarimetric data

Cheng Wang, Liang Chen, Haisheng Zhao, Wulong Guo

Proceedings Volume 10788, 107880N (2018) https://doi.org/10.1117/12.2325061

KEYWORDS: Synthetic aperture radar, Scattering, Magnetism, Radar, Global Positioning System, Polarimetry, L band, Spatial resolution, Scatter measurement

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The effects of the ionosphere on spaceborne synthetic aperture radar (SAR) systems have received attention since the development of ALOS PALSAR (L-band). One of them is the Faraday rotation (FR) due to the dispersive nature of ionosphere and the existence of Earth’s magnetic field. The FR error is obviously embedded in polarimetric data of PALSAR systems, which destroys the scattering matrix. Nevertheless, distorted echoes contain abundant ionospheric information, the ionospheric sounding based on the scattering matrix data can become possible if the mechanisms of ionospheric interference can be understood and accurately modeled. SAR systems are generally characterized by high spatial resolution, this powerful technique can detect kilometer-scale ionospheric information where such unprecedented spatial resolution was previously inaccessible (e.g., such resolution is 1 to 2 orders of magnitude higher than that obtained by GPS). In this paper, by using the ALOS PALSAR full-polarization data sets, we quantitatively evaluate the reliability and accuracy of retrieved TEC information. We have used the observation results of incoherent scattering radar (ISR) to verify the accuracy of our results, given that ISR is currently the most powerful ground means for ionospheric monitoring and the ideal means for ionospheric data verification. In our work, the AMISR data for the Alaskan region in the United States is selected. After screening the data, we have selected and compared three sets of SAR and ISR results obtained at the same observation time (universal time: 8/6/2010, 21:6:25; 3/19/2011, 7:32:50; 3/31/2011, 7:28:16) and place. Our results show that the deviation between the results of SAR and ISR is only 0.1-0.35 TECU accounting for different factors, such as system and geographical deviations. However, the accuracy of the most widely used GPS data can only up to 1-2 TECU. Both accuracy and resolution of ionospheric sounding using fullpolarization SAR are therefore superior to those of ionospheric sounding using GPS.

Proceedings Article | 8 November 2014 Paper

Analysis of soil moisture retrieval from airborne passive/active L-band sensor measurements in SMAPVEX 2012

Liang Chen, Hongting Song, Lei Tan, Yinan Li, Hao Li

Proceedings Volume 9260, 92600S (2014) https://doi.org/10.1117/12.2071225

KEYWORDS: Soil science, L band, Vegetation, Data modeling, Sensors, Radiometry, Scattering, Algorithm development, Surface roughness, Databases

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