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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Feature Extraction for Hyperspectral Images Using Local Contain Profile
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Feature Extraction for Hyperspectral Images Using Local Contain Profile

机译:使用本地包含配置文件的高光谱图像的特征提取

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摘要

Spectral-spatial information extraction is always important for hyperspectral image analysis, such as classification and detection. Extinction profile (EP), based on component tree (max-tree/min-tree), has been recently-proposed as one of the best morphological feature extraction methods. As an alternative, a new local contain profile (LCP), employing topology tree in the tree generation process, has been proposed. Topology tree, including the tree of shapes and the inclusion tree, is constructed by the inclusion relationship between the connected components belonging to the same level in the image. Furthermore, several new morphological properties, such as compactness, and elongation, are designed to accurately exploit specific shape information. The proposed LCP is expected to discard some irrelevant spatial information while preserving useful spatial characteristics. Experimental results validated on several real hyperspectral data demonstrate that the proposed LCP can significantly improve accuracy and decrease the half of feature dimension when compared to the state-of-the-art EP.
机译:光谱空间信息提取始终是高光谱图像分析的重要性,例如分类和检测。基于组分树(MAX树/ MIN树)的消灭概况(EP)已被最近推出作为最佳形态特征提取方法之一。作为替代方案,已经提出了一种新的本地包含在树生成过程中使用拓扑树的简介(LCP)。包括形状树和包含树的拓扑树是由所属于图像中相同级别的连接组件之间的包含关系构成的。此外,诸如紧凑性和伸长率的几种新的形态学性质被设计成准确地利用特定形状信息。预计所提出的LCP将丢弃一些无关的空间信息,同时保持有用的空间特征。在几个实际高光谱数据上验证的实验结果表明,与最先进的EP相比,所提出的LCP可以显着提高精度并降低特征尺寸的一半。

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