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首页> 外文期刊>Journal of Applied Remote Sensing >Applicability of data mining algorithms in the identification of beach features/patterns on high-resolution satellite data
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Applicability of data mining algorithms in the identification of beach features/patterns on high-resolution satellite data

机译：数据挖掘算法在高分辨率卫星数据上海滩特征/模式识别中的适用性

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摘要

The available beach classification algorithms and sediment budget models are mainly based on in situ parameters, usually unavailable for several coastal areas. A morphological analysis using remotely sensed data is a valid alternative. This study focuses on the application of data mining techniques, particularly decision trees (DTs) and artificial neural networks (ANNs) to an IKONOS-2 image in order to identify beach features/patterns in a stretch of the northwest coast of Portugal. Based on knowledge of the coastal features, five classes were defined. In the identification of beach features/patterns, the ANN algorithm presented an overall accuracy of 98.6% and a kappa coefficient of 0.97. The best DTs algorithm (with pruning) presents an overall accuracy of 98.2% and a kappa coefficient of 0.97. The results obtained through the ANN and DTs were in agreement. However, the ANN presented a classification more sensitive to rip currents. The use of ANNs and DTs for beach classification from remotely sensed data resulted in an increased classification accuracy when compared with traditional classification methods. The association of remotely sensed high-spatial resolution data and data mining algorithms is an effective methodology with which to identify beach features/patterns. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：可用的海滩分类算法和沉积物预算模型主要基于原位参数，通常不适用于几个沿海地区。使用遥感数据进行形态分析是一种有效的选择。这项研究的重点是将数据挖掘技术（尤其是决策树（DTs）和人工神经网络（ANN））应用于IKONOS-2图像，以识别葡萄牙西北海岸一带的海滩特征/模式。基于对沿海特征的了解，定义了五个类别。在海滩特征/模式识别中，ANN算法的总体准确度为98.6％，kappa系数为0.97。最好的DTs算法（带修剪）表现出98.2％的整体准确度和0.97的kappa系数。通过ANN和DT获得的结果是一致的。然而，人工神经网络提出了一个对裂隙电流更敏感的分类。与传统的分类方法相比，使用ANN和DT对来自遥感数据的海滩进行分类可提高分类的准确性。遥感高空间分辨率数据与数据挖掘算法的结合是一种有效的方法，可用于识别海滩特征/模式。 （C）2015年光电仪器工程师协会（SPIE）

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