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首页> 外文期刊>International journal of remote sensing >Multi-scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF-SVM
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Multi-scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF-SVM

机译：基于GMRF-SVM的高分辨率遥感影像多尺度信息提取与区域划分方法

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This paper proposes the work flow of multi-scale information extraction from high resolution remote sensing images based on features: rough classification -parcel unit extraction (subtle segmentation) - expression of features - intelligent illation - information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)-Support Vector Machine (SVM), that is the image classification based on GMRF-SVM. This method integrates the advantages of GMRF-based texture classification and SVM-based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF-based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition.
机译：本文提出了基于以下特征的高分辨率遥感影像多尺度信息提取的工作流程：粗分类-包裹单位提取（细微分割）-特征表达-智能不适-信息提取或目标识别。然后，本文分析了其对于从海量数据中大规模提取信息的理论和实践意义。基于图像的光谱和纹理，提出了一种基于高斯马尔可夫随机场（GMRF）-支持向量机（SVM）的高分辨率遥感图像区域划分方法，即基于GMRF-SVM的图像分类方法。该方法将基于GMRF的纹理分类和基于SVM的模式识别的优点与小样本集成在一起，并使其易于利用先验知识。最后，论文报告了对Ikonos图像的测试。实验结果表明，本文所使用的方法在时间开销和处理效果方面均优于基于GMRF的分割方法。另外，它对于信息提取和目标识别的阶段实际上是有意义的。

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