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Data fusion and multisource image classification
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DOI：10.1080/0143116031000115111.png)
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摘要
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The aim of this study is to explore different data fusion techniques and compare the performances of a standard supervised classification and expert classification. For the supervised classification, different feature extraction approaches are used. To increase the reliability of the classification, different threshold values are determined and fuzzy convolutions are applied. For the expert classification, a set of rules is determined and a hierarchical decision tree is created. Overall, the research indicates that multisource information can significantly improve the interpretation and classification of land cover types and the expert classification is a powerful tool in the production of a reliable land cover map.
Keyword:
RADAR IMAGERY
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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.6
论文数:
1.2W
被引数:
2.7W
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引用论文
Multisensor image fusion in remote sensing: concepts, methods and applications遥感多传感器图像融合: 概念、方法与应用
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