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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Classification of natural areas in northern Finland using optical remote sensing images and data fusion
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Classification of natural areas in northern Finland using optical remote sensing images and data fusion

机译：光纤遥感图像和数据融合，芬兰北部自然区域分类

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The aim of this study was to make CORINE 2000 classification better in Northern Finland by making the classes of natural areas more separable using optical remote sensing images and decision based data fusion methods. Phenological time-series derived from MODIS images and ETM-image mosaic were classified using Maximum Likelihood classifier. Classification results were merged using different data fusion methods and their result compared. The used methods were not particularly successful, because the overall accuracy of CORINE 2000- classification, 52%, was better than the overall accuracies of spectral classifications and data fusion methods. The best data fusion methods were maximum joint a posteriori probabilityclassification and classification where a priori probabilities have been acquired from lower resolution classification.
机译：本研究的目的是通过使用光学遥感图像和基于决策的数据融合方法使自然区域的阶级更可分离，使芬兰北部更好地在芬兰进行康塞2000年分类。使用最大似然分类器分类了从MODIS图像和ETM图像马赛克的授权时间序列。使用不同的数据融合方法和结果合并分类结果。二手方法没有特别成功，因为鲤鱼的总体精度为52％，优于光谱分类和数据融合方法的总体精度。最佳数据融合方法是最大关节的后验概率分类和分类，其中已经从较低的分辨率分类获取了先验概率。

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