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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Estimating Soil Moisture With the Support Vector Regression Technique
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Estimating Soil Moisture With the Support Vector Regression Technique

机译：支持向量回归技术估算土壤湿度

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摘要

This letter presents an experimental analysis of the application of the $ varepsilon$-insensitive support vector regression (SVR) technique to soil moisture content estimation from remotely sensed data at field/basin scale. SVR has attractive properties, such as ease of use, good intrinsic generalization capability, and robustness to noise in the training data, which make it a valid candidate as an alternative to more traditional neural-network-based techniques usually adopted in soil moisture content estimation. Its effectiveness in this application is assessed by using field measurements and considering various combinations of the input features (i.e., different active and/or passive microwave measurements acquired using various sensor frequencies, polarizations, and acquisition geometries). The performance of the SVR method (in terms of estimation accuracy, generalization capability, computational complexity, and ease of use) is compared with that achieved using a multilayer perceptron neural network, which is considered as a benchmark in the addressed application. This analysis provides useful indications for building soil moisture estimation processors for upcoming satellites or near-real-time applications.
机译：这封信提供了对varepsilon不敏感支持向量回归（SVR）技术在从田间/盆地规模的遥感数据估算土壤含水量中的应用的实验分析。 SVR具有吸引人的特性，例如易用性，良好的固有泛化能力以及对训练数据中噪声的鲁棒性，这使其成为替代土壤水分含量估算中通常采用的基于传统神经网络技术的有效选择。 。通过使用现场测量并考虑输入特征的各种组合（即使用各种传感器频率，极化和采集几何形状采集的不同有源和/或无源微波测量）来评估其在该应用中的有效性。将SVR方法的性能（在估计准确性，泛化能力，计算复杂性和易用性方面）与使用多层感知器神经网络实现的性能进行了比较，后者被视为所解决应用中的基准。该分析为构建用于即将到来的卫星或近实时应用的土壤湿度估算处理器提供了有用的指示。

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