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中文摘要：
快速、准确地从遥感影像中提取感兴趣的关键信息是遥感对地观测领域的重要研究内容。随着当代遥感传感器技术呈现出高光谱分辨率、高空间分辨率和高时间分辨率的新特点，传统的人工解译遥感影像的方式已经很难应用，取而代之的是计算机全自动提取遥感影像信息的新方法。虽然现在计算机硬件的发展已经为快速、准确处理海量遥感影像提供了物理基础，但是相应的数据处理算法普遍存在自适应能力不足的缺点。因此，本论文基于机器学习与模式识别领域的新方法，以数据的张量表达和流形学习为研究主线，结合正则化理论、稀疏学习、迁移学习等新方法，开展遥感影像信息提取中的流形学习方法研究。主要研究内容如下：
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