星载合成孔径雷达图像小特征的准确分割方法

doi:10.13229/j.cnki.jdxbgxb20171002

摘要/Abstract

摘要：

星载合成孔径雷达(SAR)图像信息丰富且复杂,无法通过某种信息对其所有目标进行准确表述,相同种类图像有很大不同,而不同种类图像有很大的相似性,传统方法无法解决上述问题,导致分割精度低。为此,本文提出一种新的星载SAR图像小特征准确分割方法。首先,对星载SAR图像灰度特征进行提取,通过灰度共生矩阵对纹理特征进行提取,利用谱嵌入式聚类方法塑造星载SAR图像可识别特征轮廓模型,实现轮廓特征的提取。然后,通过支持向量机实现星载SAR图像小特征初分割,利用不同小特征信息对各过分割部分的空间关系进行研究,在全局范围对类似过分割部分进行迭代增长处理,对星载SAR图像进行小特征准确分割。最后通过试验证明了本文方法有很高的准确性。

关键词: 信息处理技术, 星载合成孔径雷达, 图像小特征, 准确分割

Abstract:

The image information of spaceborne synthetic aperture radar is rich and complex, and no information can be used to accurately express all the targets. The images of the same kind are very different, and the images of different kinds have great similarity. The traditional method can not solve the above problems, which leads to the low cutting precision. To solve this problem, a new small feature segmentation method for spaceborne SAR image is proposed. The gray-scale feature of the spaceborne SAR image is extracted, and the texture features are extracted by gray-scale symbiotic matrix. The feature contour model of spaceborne synthetic aperture radar image can be identified by spectral embedded clustering method, and the contour feature is extracted. The SVM is used to segment the small feature of spaceborne SAR image, and the spatial relationship of the over segmented parts is studied by using different small feature information. In the global range, the similar over segmentation parts are processed by iterative growth, and the small feature segmentation is carried out on the spaceborne synthetic aperture radar image. Experimental results show that the proposed method has high accuracy.

Key words: information procession technology, spaceborne synthetic aperture radar, image small feature, accuracy segmentation

中图分类号:

TP391

陆智俊,钟超,吴敬玉. 星载合成孔径雷达图像小特征的准确分割方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1925-1930.

LU Zhi-jun,ZHONG Chao,WU Jing-yu. Small feature segmentation method for Spaceborne SAR images[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1925-1930.

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