基于层次贝叶斯模型聚合的高光谱遥感中悬浮物总浓度的最优多波段比分析

IF 2.4 3区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Hui Ying Pak , Adrian Wing-Keung Law , Weisi Lin
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引用次数: 0

摘要

水质监测在水资源管理和水治理中发挥着重要作用。目前,监测通常通过现场采样和/或设立测量站进行,这可能是劳动密集型的,成本高昂。最近,利用无人机和高光谱传感器通过遥感监测水质的可能性显示出了巨大的前景,其关键优势是更大的空间覆盖范围,以及更高的光谱分辨率和更广泛的数据可能带来的更高的精度。相应地,需要建立更先进的方法来进行高光谱分析,以确定水质,从而利用这些丰富的信息。在这项研究中,开发了一种新的方法,称为分层贝叶斯模型聚合用于最优多波段比率分析(HBMA-OMBRA),作为从高光谱数据中估计总悬浮固体(TSS)浓度的概念验证。该方法利用了竞争模型的贝叶斯集合,因为没有一个适用于所有情况的最佳工作模型。它还包括一种称为集合带比选择(ENBRAS)的新方法,用于通过一组集合和“装袋”程序来识别最佳候选带比(BBR),然后是修改的Batchelor-Wilkin算法来对候选带比进行聚类。本研究进行了实验室调查,测量了不同环境条件下不同实验的高光谱反射率,以验证HBMA-OMBRA的稳健性。根据实验结果,使用ENBRAS识别了六个不同的候选BBR簇。特别是,红色、绿色和近红外光谱中的两个星团显示出最大的贡献。多聚类的重要性为文献中先前报道的对比结果提供了解释,并为调和这些发现提供了一些证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Retrieval of total suspended solids concentration from hyperspectral sensing using hierarchical Bayesian model aggregation for optimal multiple band ratio analysis

Retrieval of total suspended solids concentration from hyperspectral sensing using hierarchical Bayesian model aggregation for optimal multiple band ratio analysis

Water quality monitoring plays an essential role in water resource management and water governance. At present, the monitoring is commonly conducted via in-situ sampling and/or by setting up gauging stations, which can be labour intensive and costly. Recently, the possibility of monitoring water quality through remote sensing with Unmanned Aerial Vehicles (UAVs) and hyperspectral sensors has shown great promise, with the key advantages of larger spatial coverage and possibly higher accuracy enabled by higher spectral resolution and more extensive data. Correspondingly, more advanced methods need to be established for hyperspectral analysis for water quality determination to capitalize on this wealth of information. In this study, a new method called Hierarchical Bayesian Model Aggregation for Optimal Multiple Band Ratio Analysis (HBMA-OMBRA) has been developed as a proof-of-concept for estimating Total Suspended Solids (TSS) concentrations from the hyperspectral data. The method leverages on the Bayesian ensembling of competing models because there is not a single best working model for all situations. It also encompasses a new approach called Ensemble Band Ratio Selection (ENBRAS) for the identification of best candidate band ratios (BBRs) via a set of ensembling and “bagging” procedures, followed by a modified Batchelor Wilkin’s algorithm to cluster the candidate band ratios. A laboratory investigation was conducted in the present study to measure the hyperspectral reflectance in different experiments under various environmental conditions to verify the robustness of HBMA-OMBRA. From the experimental results, six distinct clusters of candidate BBRs were identified using ENBRAS. In particular, two clusters in the red, green, and near infrared spectrum showed the largest contribution. The significance of multi-clusters provides an explanation for previously contrasting results reported in the literature and some evidence for reconciling these findings.

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来源期刊
Journal of Hydro-environment Research
Journal of Hydro-environment Research ENGINEERING, CIVIL-ENVIRONMENTAL SCIENCES
CiteScore
5.80
自引率
0.00%
发文量
34
审稿时长
98 days
期刊介绍: The journal aims to provide an international platform for the dissemination of research and engineering applications related to water and hydraulic problems in the Asia-Pacific region. The journal provides a wide distribution at affordable subscription rate, as well as a rapid reviewing and publication time. The journal particularly encourages papers from young researchers. Papers that require extensive language editing, qualify for editorial assistance with American Journal Experts, a Language Editing Company that Elsevier recommends. Authors submitting to this journal are entitled to a 10% discount.
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