Ms. Yu Xing Profile

Yu Xing

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Proceedings Article | 3 June 2024 Paper

Comparative study of image fusion methods for WorldView-3 data

Yaqin Sun, Baogang Di, Yu Xing, Fan Bu

Proceedings Volume 13170, 131701Q (2024) https://doi.org/10.1117/12.3032124

KEYWORDS: Image fusion, Short wave infrared radiation, Principal component analysis, Infrared imaging, Remote sensing, Data fusion, Spatial resolution, Multispectral imaging

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WorldView-3 is one of the highest spatial resolution multispectral commercial satellites currently available on the market, with great potential for applications in land analysis, geological mineral surveys, and disaster assessments. In order to fully utilize the high spatial resolution of WorldView-3 data, fusion processing was performed using Principal Component Analysis (PCA), Gram-Schmidt (G-S), Histogram Contrast Stretching (HCS), and Nearest-Neighbor Diffusion (NNDiffuse) methods to combine the multispectral images with the panchromatic image. Qualitative and quantitative evaluations were conducted on the fused images, and the results showed that in the visible-near infrared (VNIR) wavelength range, the HCS method had the best overall fusion effect, with the closest mean value to the original multispectral image, highest standard deviation, information entropy, and average gradient value. The NNDiffuse method had the highest correlation coefficient and spectral fidelity between the fused image and the original multispectral image. In the shortwave infrared wavelength (SWIR) bands, the HCS fused image had the best spectral and color fidelity, while the NNDiffuse and G-S methods produced fused images with richer information and hierarchy, higher clarity, and more distinct spatial structure and texture features.

Proceedings Article | 19 January 2024 Paper

Remote sensing monitoring of coal mining collapse and analysis of ecological restoration model in China

Yaqin Sun, Yu Xing, Jinzhong Yang

Proceedings Volume 12980, 129801F (2024) https://doi.org/10.1117/12.3020897

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Long-term, extensive overexploitation of coal mining resources has negatively impacted the ecological environment while bringing about economic prosperity. This has resulted in frequent mining geological disasters, particularly coal mining collapses, endangering the lives and property of locals and necessitating an urgent need for ecological restoration and all-encompassing management. For ecological restoration and thorough management, it is necessary to map out information on coal mining collapse on a national scale, including the location, scale, and waterlogging situation. On a national level, however, relatively few academics have examined coal mining collapse until this point. This study uses domestic high-resolution remote sensing data to track coal mining collapse across the country. The findings reveal that by the end of 2018, there were 35,453 coal mining collapse areas with an area of 21,957 km² ; the area of collapse pits was 14,859 km² , the area of waterlogged collapse pits was 1,716 km² , the area of restoration and treatment of collapse pits was 2,712 km² , and the overall rate of restoration and treatment of collapses was 15.43%. The ecological restoration models of coal mining collapses may be essentially categorized into five categories: the reclamation models for agriculture and forestry, fisheries and ecological wetland, landscape management, urban development, and new energy industry. The findings of this study can, to some extent, be used as data support for the supervision and thorough prevention and control of coal mining collapse in China as well as a source of reference for the ecological restoration of coal mining collapse, both of which have research significance.

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