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CLASSIFICATION OF POLAR SATELLITE DATA USING IMAGE FEATURES AND DECISION TREE CLASSIFIER

机译:利用图像特征和决策树分类器对极地卫星数据进行分类

摘要

In the polar region, it is difficult to discriminate between clouds and ground surface from satellite visible or infrared data, because of the high albedo and low surface temperature of snow and ice cover. In this paper, a method to classify clouds, sea ice and ground is proposed. This study is based upon analysis of the NOAA/AVHRR infrared images in Antarctica. The algorithm consists of two major approaches : estimating image features and a classification algorithm. A decision tree classifier is designed to classify the region into one of three classes using six image features. Though sea ice and ground can be largely separated using only one feature, more than three features are necessary to separate clouds.
机译:在极地地区,由于高反照率和较低的冰雪覆盖表面温度,很难通过卫星可见或红外数据来区分云层和地表。本文提出了一种对云,海冰和地面进行分类的方法。这项研究基于对南极NOAA / AVHRR红外图像的分析。该算法包括两种主要方法:估计图像特征和分类算法。决策树分类器旨在使用六个图像特征将区域分类为三个类别之一。尽管仅使用一种功能就可以在很大程度上分离海冰和地面,但是要分离云,则需要三个以上的功能。

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