高分影像信息提取的特征结构化多尺度分析建模方法研究

Modeling Method of Structured Feature Multi-scale Analysis for High Resolution Remote Sensing Image Information Extraction

  • 摘要: 围绕高分影像丰富的细节信息和多分辨率特征多尺度分析与信息提取的建模方法开展研究。首先研究高分影像特征分解与表达的变换域方法,构成多尺度、多通道、多层级的变换特征,然后分别使用下采样、上采样和非下采样方法进行特征结构化,最后建立特征结构化多尺度分析模型。并对直塔模型进行具体建模过程分析与实验研究,验证了特征结构化多尺度分析模型方法的有效性。结果表明,该方法可以增强高分影像多尺度分析的灵活性,并有效解决其多尺度分析与信息提取问题。

     

    Abstract: This paper studies modeling method of multi-scale analysis and information extraction, which based on rich detail information and multi-resolution features of high resolution remote sensing image. Firstly, this dissertation describes a transform domain method for feature decomposition and expression of high resolution remote sensing image, which comprises a multi-scale, multi-channel, and multi-layer transformation feature. Then, subsampling, upsampling, and non-subsampling me-thods are applied to construct the feature structure respectively. Finally, a feature structured multi-scale analysis model is set up. The validity of the feature structured multi-scale analysis model is verified by the modeling process analysis and experimental research of the straight tower model. The result indicates that the proposed method can enhance the flexibility of multi-scale analysis of high resolution image and solve the problem of multi-scale analysis and information extraction effectively.

     

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