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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >A Data-Mining Technique for Aerosol Retrieval Across Multiple Accuracy Measures
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A Data-Mining Technique for Aerosol Retrieval Across Multiple Accuracy Measures

机译：跨多种精度度量的气溶胶提取数据挖掘技术

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摘要

A typical approach in supervised learning is to select an accuracy measure and train a predictor that maximizes it. This can be insufficient in remote-sensing applications where predictor performance is often evaluated over multiple domain-specific accuracy measures. Here, we test the hypothesis that predictors can be trained to maximize performance over multiple accuracy measures. To do this, we evaluate several metalearning algorithms on the problem of aerosol optical depth (AOD) retrieval. The multiple accuracy measures included mean squared error, correlation, relative squared error, and fraction of satisfactory predictions. The proposed metalearning algorithms have a two-layer architecture, where the first layer consists of multiple neural networks, each trained using a different accuracy measure, and the second layer aggregates decisions of the first layer predictors. To evaluate AOD predictors, we used nearly 70 000 collocated data points whose attributes were radiances, solar and view angles, and terrain elevation from MODerate resolution Imaging Spectrometer (MODIS) instrument satellite observations and whose target AOD variable was obtained from the ground-based AEROsol robotic NETwork (AERONET) instruments. The data were collected at 221 AERONET locations over the globe in the period between 2005 and 2007. AOD prediction accuracies of neural networks were compared to the recently developed operational MODIS C005 retrieval algorithm and to several other data-mining methods. Results showed that neural networks are better at reproducing the test data than the operational retrieval algorithm and that predictors obtained by metalearning are robust over multiple accuracy measures.
机译：监督学习中的一种典型方法是选择一个准确度度量并训练一个最大化它的预测器。这在遥感应用中可能是不够的，在这些应用中，经常通过多个特定于域的精度度量来评估预测器性能。在这里，我们测试了可以对预测变量进行训练以在多个准确性度量上最大化性能的假设。为此，我们评估了关于气溶胶光学深度（AOD）检索问题的几种金属学习算法。多种准确性度量包括均方误差，相关性，相对平方误差和令人满意的预测的分数。所提出的金属学习算法具有两层体系结构，其中第一层由多个神经网络组成，每个神经网络使用不同的精度度量进行训练，第二层则汇总第一层预测变量的决策。为了评估AOD预报器，我们使用了近7万个并置数据点，这些数据点的特征是辐射度，太阳角和视角以及MODerate分辨率成像光谱仪（MODIS）仪器卫星观测的地形高程，并且目标AOD变量是从地面AEROsol获得的机器人网络（AERONET）仪器。在2005年至2007年期间，在全球221个AERONET位置收集了数据。将神经网络的AOD预测精度与最近开发的可操作MODIS C005检索算法以及其他几种数据挖掘方法进行了比较。结果表明，与可操作的检索算法相比，神经网络在重现测试数据方面更胜一筹，并且通过金属切削获得的预测变量在多种精度测量中均具有较强的鲁棒性。

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