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首页> 外文会议>Geoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International >Snow classification from SSM/I data over varied terrain using an artificial neural network classifier
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Snow classification from SSM/I data over varied terrain using an artificial neural network classifier

机译：使用人工神经网络分类器根据不同地形上的SSM / I数据进行雪分类

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摘要

The brightness temperatures (Tbs) observed by the Special Sensor Microwave/Imager (SSM/I) radiometer are sensitive to the changes in land surface snow conditions. Previously developed SSM/I snow classification algorithms have limitations and do not work properly for terrain where forests overlay snow cover. In this study, the authors applied unsupervised cluster analysis to define 6 snow classes in Tb observations, assessing both sparseand medium-vegetated region classes. Typical SSM/I Tb signature, in terms of cluster means, of each snow class was determined by calculating the mean Tbs of the corresponding cluster. A single-hidden-layer backpropagation (backprop) artificial neural network (ANN) classifier was designed to learn the 6 Tb patterns. Classification performance, in terms of error rate (%), was as small as 2.4%. This study confirms the potential of using cluster means in ANN supervised learning, and suggests a nonlinear retrieval method towards making the inferences of snow classes from SSM/I data over varied terrain operational. Improvement is expected by identifying more SSM/I Tb signatures of different land surface types to train the ANN classifier.
机译：特殊传感器微波/成像仪（SSM / I）辐射计观测到的亮温（Tbs）对地面雪状况的变化很敏感。先前开发的SSM / I雪分类算法具有局限性，不适用于森林覆盖雪盖的地形。在这项研究中，作者应用无监督聚类分析在Tb观测中定义了6个降雪类别，同时评估了稀疏和中等植被区域的等级。通过计算相应聚类的平均Tb，可以确定每个雪类的典型SSM / I Tb签名（按聚类平均值）。设计了一个单层反向传播（backprop）人工神经网络（ANN）分类器，以学习6种Tb模式。就错误率（％）而言，分类性能仅为2.4％。这项研究证实了在人工神经网络监督学习中使用聚类方法的潜力，并提出了一种非线性检索方法，可以根据不同地形上的SSM / I数据进行雪类推断。通过识别更多不同地表类型的SSM / ITb签名以训练ANN分类器，有望实现改善。

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