Hyper-spectral remote sensing water depth retrieval based on spectral difference factors

Zhen Tian; Yi Ma; Jianhua Zhu

doi:10.1117/12.2505649

12 December 2018 Hyper-spectral remote sensing water depth retrieval based on spectral difference factors

Zhen Tian, Yi Ma, Jianhua Zhu

Proceedings Volume 10846, Optical Sensing and Imaging Technologies and Applications; 1084632 (2018) https://doi.org/10.1117/12.2505649
Event: International Symposium on Optoelectronic Technology and Application 2018, 2018, Beijing, China

Abstract

Many studies have indicated that spectrum is mainly decided by substratum and water depth in shallow water，so spectrum above one kind of substrate is only decided by water. According to this idea we studied the technology of substrate classification, as well as analyzed the impacts of various water-depth extraction factors on the inversion accuracy. The following results have been obtained. (1) SVM has the highest classification accuracy, whose Kappa coefficient was 0.86 and overall accuracy was 92.34%, which is higher than that of neural network and maximum likelihood. (2) Correlation coefficient between factors based on spectral shape and water depth were over 70%, which is higher than that based on spectral amplitude. (3) SA and SGA are all have an exponential correlation with water depth and their inversion accuracy was almost the same. The mean relative error and mean absolute error for two factors were 9.9%,0.61m and 7.3%,0.74m, respectively. But they have different performance in various substrate area and depth.

Citation Download Citation

Zhen Tian, Yi Ma, and Jianhua Zhu "Hyper-spectral remote sensing water depth retrieval based on spectral difference factors", Proc. SPIE 10846, Optical Sensing and Imaging Technologies and Applications, 1084632 (12 December 2018); https://doi.org/10.1117/12.2505649

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KEYWORDS

Remote sensing

LIDAR

Data modeling

Neural networks

Data corrections

Algorithm development

Hyperspectral imaging

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