Prof. Baohua Zhang Profile

Prof. Baohua Zhang

at Inner Mongolia Univ of Science and Technology

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Author

Publications (3)

SPIE Journal Paper | 10 March 2023

Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

Ming Zhang, Yang Chen, Xiaoqi Lv, Lidong Yang, Dahua Yu, Jianjun Li, Baohua Zhang

JARS, Vol. 17, Issue 01, 016511, (March 2023) https://doi.org/10.1117/12.10.1117/1.JRS.17.016511

KEYWORDS: Synthetic aperture radar, Object detection, Target detection, Education and training, Detection and tracking algorithms, Visualization, Image fusion, Deep learning, Visual process modeling, Performance modeling

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SPIE Journal Paper | 27 October 2022

Seasonal variations of canopy spectra and their indications to carbon fluxes in a temperate forest in northeast China

Jie Jiang, Quanzhou Yu, Yujie Liu, Tianquan Liang, Qingxin Tang, Lei Zhou, Hongli Zhang, Baohua Zhang, Shaoqiang Wang

JARS, Vol. 16, Issue 04, 048503, (October 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.048503

KEYWORDS: Carbon, Reflectivity, Vegetation, Molybdenum, Remote sensing, Ecosystems, Picosecond phenomena, Near infrared, Visible radiation, Nitrogen

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The seasonal variations of forest canopy spectral characteristics are critical to improving the utilization of remote sensing methodology to quantify forest physiology, especially forest carbon sink. However, the seasonal variations of forest canopy spectra are poorly understood. Combined field survey and EO-1 Hyperion imageries, we extracted the spectral curves of seven forest types of Changbai Mountain in China in seven periods. We also calculated various remote sensing indexes and analyzed their seasonal change of spectral characteristics among different forest types. Optimal indexes were selected to indicate the seasonal variation of forest carbon fluxes. Our results showed that there were differences in spectral curves among forest types. The reflectance of coniferous forests was lower than that of broad-leaved forests in growing season. Changbai Scotch pine forest owned the lowest spectral reflectance, whereas the reflectance of Mongolian oak forest was the highest, especially in the near-infrared region. The red edge slope (RES) of broad-leaved forest was higher than coniferous forest in spring and summer. The RES of broad-leaved and coniferous forests was similar in autumn. The red edge position of various forest types showed slight shift in different seasons. Four typical forest types showed different spectral characteristics with seasonal changes. The seasonal variation of coniferous forest spectral curves was not obvious. The seasonal variation of broad-leaved forest spectra was the largest. Most of the spectral indexes can indicate the seasonal variation characteristics of each forest type. Enhanced vegetation index (EVI) is better than normalized difference vegetation index (NDVI) to indicate the forest phenology. Seasonal curves of spectral indexes were different in all forest types. Spectral indexes of coniferous forests were most stable throughout the year. The curves of each index in broad-leaved forests showed significant difference in autumn, which may be influenced by the understory vegetation after their defoliation. For broad-leaved Korean (BK) pine forest, the scaled value of photochemical reflectance index (SPRI)*EVI owned the highest correlation with gross primary productivity (R = 0.99 and P < 0.01) and net ecosystem exchange (R = − 0.77 and P < 0.05), respectively. SPRI*NDVI showed the highest correlation with ecosystem respiration (R = 0.96 and P < 0.01). The seasonal variation of carbon fluxes of different forest types retrieved from the optimal remote sensing index were consistent, but their peaks occurred at different times.

Proceedings Article | 17 April 2020 Paper

Research on the full stokes parameters from ocean targets using the laboratory simulated polarimetry system

Tianquan Liang, Baohua Zhang, Zhenfang He, Qingxin Tang, Yuxin Hao, Hongzhu Ji

Proceedings Volume 11455, 114551C (2020) https://doi.org/10.1117/12.2560311

KEYWORDS: Polarization, Target detection, Polarimetry, Light sources, Water, Scattering, Light scattering, Ocean optics, Polarizers

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