Prof. Jun Pan Profile

Prof. Jun Pan

at Jilin Univ

SPIE Involvement:

Author

Publications (4)

SPIE Journal Paper | 1 October 2024

Sea ice thickness level inversion model based on Landsat 8 data in Bohai Sea, China

Hanzhe Jiang, Jun Pan, Lijun Jiang, Shengyu Sun, Yehan Sun

JARS, Vol. 18, Issue 03, 032405, (October 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.032405

KEYWORDS: Ice, Data modeling, Visible radiation, Reflectivity, Landsat, Remote sensing, Matrices, Factor analysis, Clouds, Correlation coefficients

Read Abstract +

Remote sensing retrieval of sea ice thickness can quickly and accurately obtain the spatiotemporal distribution information of large sea areas. This has important implications for marine resource utilization and global climate change impacts. This study aims to take the Bohai Sea as an example, use Landsat 8 data, and analyze the spectral characteristics of sea ice to establish sensitive indicators for sea ice thickness remote sensing retrieval and determine the functional relationship between these indicators and sea ice thickness. This paper first uses variance analysis to select the sensitive wavebands for sea ice thickness inversion and then uses factor analysis to conduct a comparative sensitivity analysis on various combinations of sensitive wavebands. Subsequently, the relationship between sea ice thickness and sensitive zones was quantitatively analyzed through regression analysis. Finally, the accuracy of the model was verified using measured data of sea ice thickness in the Bohai Sea in 2016 and 2023, compared with the existing albedo model, and the application of the model was demonstrated. The experimental results of variance analysis and factor analysis show that the visible light band shows strong sensitivity to sea ice thickness, among which the red light band is the most sensitive. The linear-weighted combination of visible light bands (B1-coastal, B2-blue, B3-green, and B4-red) shows a significant linear correlation with sea ice thickness, with a linear regression square value as high as 0.9709. Experimental verification shows that there is a significant linear correlation between the sea ice thickness inverted by each model and the measured thickness data. Research conclusions: (1) variance analysis and factor analysis methods can effectively select sensitive bands and evaluate the inversion effect of band combinations. (2) Either a single red light band or a linear combination of visible light bands can be used as a sensitive indicator for remote sensing retrieval of sea ice thickness. (3) There is a significant linear correlation function between sensitive indicators and sea ice thickness. (4) The measured data confirms the accuracy of the model. Compared with the albedo model, the inversion model proposed in this article is more sensitive to sea ice thickness and is less affected by clouds and sediment. It can provide a reference for the inversion simulation of sea ice thickness and also provide a demonstration for the application of the temporal and spatial distribution laws of sea ice.

SPIE Journal Paper | 23 November 2022

Remote sensing of optical properties of smoke aerosols: simulation experiment and application based on spaceborne data

Yehan Sun, Libo Hao, Jun Pan, lijun Jiang, Jiangtao Song, He Xu, Xiaoyan Chen, Helin Wang

JARS, Vol. 16, Issue 04, 044514, (November 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.044514

KEYWORDS: Aerosols, Scattering, Optical properties, Remote sensing, Atmospheric particles, Reflectivity, Atmospheric modeling, Mass attenuation coefficient, Transmittance, Particles

Read Abstract +

Proceedings Article | 15 February 2022 Paper

Feature analysis and classification of typical mineral images based on Fourier transform

Yaqi Guo, Jun Pan, Lijun Jiang, Yehan Sun

Proceedings Volume 12161, 121610W (2022) https://doi.org/10.1117/12.2626910

KEYWORDS: Minerals, Image classification, Feldspar, Mica, Statistical analysis, Quartz, Factor analysis, Fourier transforms, Statistical methods, Feature extraction

Read Abstract +

SPIE Journal Paper | 3 June 2014 Open Access

Identification of high temperature targets in remote sensing imagery based on factor analysis

Yi-Fan Yu, Jun Pan, Li-Xin Xing, Li-Jun Jiang, Shu Liu, Yue Yuan, Hua-Liang Yu

JARS, Vol. 8, Issue 01, 083622, (June 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083622

KEYWORDS: Factor analysis, Remote sensing, Infrared radiation, Thermography, Reflectivity, Infrared imaging, Statistical analysis, Target recognition, Visualization, Vegetation

Read Abstract +

DOWNLOAD PAPER

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years