Shallow water depth inversion in Longwan Port based on WorldView-2 Remote Sensing Image

GUO Xiao-lei; QIU Zhen-ge; SHEN Wei; LUAN Kui-feng; CAO Bin-cai; WU Zhong-qiang

doi:10.3969/j.issn.1001-909X.2017.03.004

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Journal of Marine Sciences ›› 2017, Vol. 35 ›› Issue (3) : 27-33. DOI: 10.3969/j.issn.1001-909X.2017.03.004

Shallow water depth inversion in Longwan Port based on WorldView-2 Remote Sensing Image

GUO Xiao-lei^1,2, QIU Zhen-ge^*1,2, SHEN Wei^1,2, LUAN Kui-feng^1,2, CAO Bin-cai^1,2, WU Zhong-qiang^1,2

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Abstract

According to the principle of remote sensing water depth inversion, WorldView-2 multi-spectral satellite data and chart depth data of Longwan Port in Hainan Island were used. The water depths in 0-2 m, 2-5 m, 5-10 m, 10-15 m and 15-20 m were partitively treated, tidal correction,the correlation analysis and regression analysis of chart water depth and the corresponding image band reflectivity values were also developed, and a piecewise linear model was built for shallow water bathymetry with which the actual calculation of shallow water depth and precision analysis were carried out. The results show that the depth accuracy of the partitioned linear regression model is higher than that of the non-partitioned model in different water depth ranges. In the partition model, the multi-band model has the highest inversion accuracy of 0-5 m, and the double-band ratio model has the highest inversion accuracy of 5-20 m, but inversion of the depth in the most shallow yet to be improved. The water depth extracted by this method is similar to that of chart water depth data, which can meet the requirements of marine scientific research on large-scale shallow underwater detection requirements.

Key words

shallow water / remote sensing depth inversion / WorldView-2 / linear regression model

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GUO Xiao-lei, QIU Zhen-ge, SHEN Wei, LUAN Kui-feng, CAO Bin-cai, WU Zhong-qiang. Shallow water depth inversion in Longwan Port based on WorldView-2 Remote Sensing Image[J]. Journal of Marine Sciences. 2017, 35(3): 27-33 https://doi.org/10.3969/j.issn.1001-909X.2017.03.004

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