基于神经网络的水体成分检索应用于模拟沿海水体条件的辐射传输模型

Madjid Hadjal, Ross Paterson, D. McKee
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引用次数: 0

摘要

由于存在另外两种改变光信号的成分:有色溶解有机物质(CDOM)和矿物悬浮沉积物(MSS),在沿海水域使用海洋颜色遥感(OCRS)信号估计叶绿素(CHL)是困难的。人工神经网络(NNs)具有处理信号复杂性的能力,是解决这一问题的潜在方法。在这里,神经网络被开发用于在两个数据集上操作,这些数据集复制了使用Hydrolight 5.2模拟的MODIS Aqua波段。在模拟信号中加入了人工噪声以提高真实感。两个数据集使用了相同的水中成分浓度范围,不同的是对数浓度分布的类型。第一种方法使用高斯分布来模拟自然水条件下的样本。第二种方法使用平坦分布,旨在探索高斯分布中高浓度和低浓度的欠采样极值的影响。对浓度分布结构的影响进行了评估,并没有发现切换到平坦分布的好处。正态分布表现更好,因为它减少了相对难以对不同浓度的其他成分进行解析的低浓度样品的数量。在这种模拟环境中,除了沿海水域中其他成分占主导地位的光信号值较低外,神经网络与真实的原位算法相比具有出色的估计CHL的能力。使用神经网络也可以以非常高的精度预测CDOM和MSS。研究发现,使用多任务学习(MTL)同时检索所有三个成分并不比单参数检索提供任何优势。最后发现,增加波段的数量通常会提高神经网络的性能,尽管超过8个波段的回报似乎会递减。研究还表明,较少数量的精心挑选的频带比均匀分布的相同大小的频带具有更好的性能。这些结果为使用高光谱卫星传感器的神经网络的未来性能提供了有用的见解,并突出了特定波段的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditions
Estimation of chlorophyll (CHL) using ocean colour remote sensing (OCRS) signals in coastal waters is difficult due to the presence of two other constituents altering the light signal: coloured dissolved organic material (CDOM) and mineral suspended sediments (MSS). Artificial neural networks (NNs) have the capacity to deal with signal complexity and are a potential solution to the problem. Here NNs are developed to operate on two datasets replicating MODIS Aqua bands simulated using Hydrolight 5.2. Artificial noise is added to the simulated signal to improve realism. Both datasets use the same ranges of in water constituent concentrations, and differ by the type of logarithmic concentration distributions. The first uses a Gaussian distribution to simulate samples from natural water conditions. The second uses a flat distribution and is intended to allow exploration of the impact of undersampling extremes at both high and low concentrations in the Gaussian distribution. The impact of the concentration distribution structure is assessed and no benefits were found by switching to a flat distribution. The normal distribution performs better because it reduces the number of low concentration samples that are relatively difficult to resolve against varying concentrations of other constituents. In this simulated environment NNs have the capacity to estimate CHL with outstanding performance compared to real in situ algorithms, except for low values when other constituents dominate the light signal in coastal waters. CDOM and MSS can also be predicted with very high accuracies using NNs. It is found that simultaneous retrieval of all three constituents using multitask learning (MTL) does not provide any advantage over single parameter retrievals. Finally it is found that increasing the number of wavebands generally improves NN performance, though there appear to be diminishing returns beyond ∼8 bands. It is also shown that a smaller number of carefully selected bands performs better than a uniformly distributed band set of the same size. These results provide useful insight into future performance for NNs using hyperspectral satellite sensors and highlight specific wavebands benefits.
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