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Seagrass mapping using high resolution multispectral satellite imagery: A comparison of water column correction models
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-08-29 , DOI: 10.1016/j.jag.2022.102990
A. Mederos-Barrera, J. Marcello, F. Eugenio, E. Hernández

Satellite remote sensing is an efficient and economical technique for studying coastal bottoms in clear and shallow waters. Accordingly, the main objective of this study is the generation of benthic maps using high spatial resolution multispectral images from the WorldView-2/3 satellites. In this context, one of the main challenges consists of eliminating the disturbances caused in the signal by the atmosphere, the sea surface, and the water column. Regarding the water column correction, there is controversy about its effectiveness to improve the results achieved. To assess the impact of the water column correction in seagrass mapping, two coastal areas with different characteristics have been selected. Specifically, an analysis has been carried out consisting of the assessment of the Lyzenga and Sagawa water column correction models to identify the algorithm that provides the best mapping precision and, additionally, to seek if this pre-processing stage is helpful when classifying the seabed. The classification models selected for the study were: Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Subspace KNN (S-KNN). Machine learning techniques have proven to achieve better results and, in particular, SVM and KNN models provide the best overall accuracy. The results after benthic mapping have demonstrated, that image classification without water column corrections provides better accuracy (95.36% and 99.20%) than using Lyzenga (73.49% and 97.80%) or Sagawa (82.04% and 99.10%), for Case 2 and 1 waters, respectively.



中文翻译:

使用高分辨率多光谱卫星图像进行海草测绘:水柱校正模型的比较

卫星遥感是研究清澈浅水海岸底部的一种有效且经济的技术。因此,本研究的主要目标是使用来自 WorldView-2/3 卫星的高空间分辨率多光谱图像生成海底地图。在这种情况下,主要挑战之一是消除大气、海面和水柱对信号造成的干扰。关于水柱校正,对其改善所取得结果的有效性存在争议。为了评估水柱校正在海草测绘中的影响,选择了两个具有不同特征的沿海地区。具体来说,已经进行了一项分析,其中包括对 Lyzenga 和 Sagawa 水柱校正模型的评估,以确定提供最佳映射精度的算法,此外,还研究了该预处理阶段是否有助于对海床进行分类。为研究选择的分类模型是:高斯朴素贝叶斯 (GNB)、支持向量机 (SVM)、K-最近邻 (KNN) 和子空间 KNN (S-KNN)。机器学习技术已被证明可以实现更好的结果,特别是 SVM 和 KNN 模型提供了最佳的整体准确性。底栖测绘后的结果表明,对于案例 2 和 1,与使用 Lyzenga(73.49% 和 97.80%)或 Sagawa(82.04% 和 99.10%)相比,没有水柱校正的图像分类提供更好的准确度(95.36% 和 99.20%)水域,分别。

更新日期:2022-08-29
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