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Spatial data mining model for satellite image classifier.

机译:卫星图像分类器的空间数据挖掘模型。

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摘要

Spatial data mining, mining knowledge from large amounts of spatial data, is a highly demanding field. Huge amounts of spatial data have been collected in various applications, i.e. remote sensing, geographical information systems, environmental assessment and planning. The collected data exceeded human ability to analyze. This thesis shows a proposed data-mining model for mining a spatial data acquitted from a remote sensed satellite images.; The thesis is divided into six chapters. Chapter one gives an overview of spatial data mining concept and includes problem definition and research objectives. Chapter two explores the theoretical framework of spatial data mining and explains the spatial data-mining phases. Chapter three centers on an unsupervised classification technique, as an essential part of the proposed spatial data-mining model. Chapter four illustrates the proposed model with used algorithms explanation, complexity and implementation. Chapter five shows the selected dataset, applied experiments, and the results.; Finally, chapter six is the conclusion, which includes summary of work, future recommendation and limitations.
机译:从大量空间数据中挖掘知识的空间数据挖掘是一个要求很高的领域。在各种应用中,例如遥感,地理信息系统,环境评估和规划,已经收集了大量的空间数据。收集的数据超出了人类的分析能力。本文提出了一种数据挖掘模型,用于挖掘遥感卫星图像释放的空间数据。本文共分为六章。第一章概述了空间数据挖掘的概念,包括问题的定义和研究目标。第二章探讨了空间数据挖掘的理论框架,并解释了空间数据挖掘的各个阶段。第三章以无监督分类技术为中心,这是所提出的空间数据挖掘模型的重要组成部分。第四章通过算法解释,复杂度和实现对所提出的模型进行了说明。第五章介绍了选定的数据集,应用实验和结果。最后,第六章是结论,包括工作总结,未来建议和局限性。

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