杨绪峰, 林伟, 延伟东, 温金环. 采用热核特征的SAR图像目标识别[J]. 红外与激光工程, 2014, 43(11): 3794-3801.
引用本文: 杨绪峰, 林伟, 延伟东, 温金环. 采用热核特征的SAR图像目标识别[J]. 红外与激光工程, 2014, 43(11): 3794-3801.
Yang Xufeng, Lin Wei, Yan Weidong, Wen Jinhuan. SAR image target recognition based on heat kernel[J]. Infrared and Laser Engineering, 2014, 43(11): 3794-3801.
Citation: Yang Xufeng, Lin Wei, Yan Weidong, Wen Jinhuan. SAR image target recognition based on heat kernel[J]. Infrared and Laser Engineering, 2014, 43(11): 3794-3801.

采用热核特征的SAR图像目标识别

SAR image target recognition based on heat kernel

  • 摘要: 为了解决SAR图像受相干斑噪声干扰和震后发生形变而识别率偏低的问题,提出了一种新的仿射、形变不变特征-热核特征,并将该特征用于SAR图像目标识别.首先采用推广的核模糊C-均值方法分割SAR图像,提取SAR图像目标形状;接着对目标形状进行Delaunay三角剖分,采用余切权重法对Laplace-Beltrami Operator离散化,通过离散化Laplace-Beltrami Operator特征值、特征向量求每一点热核特征;然后采用谱距离公式对点点间热核距离计算,转化为距离分布表示目标形状的热核特征;最后采用L1相似性准则对图像进行相似性度量,得到识别结果.实验表明:与经典的Hu不变矩方法相比,对于仿射变换和发生形变的SAR图像,该方法都具有更高的识别率.因此,基于热核特征的SAR图像识别方法是一种更加有效的识别方法.

     

    Abstract: In order to solve low recognition rate which was caused by the speckle noise and deformation in SAR image after earthquake, a new heat kernel feature which possesses the properties of affine invariance and deformation invariance was put forward, and this feature was used in SAR image target recognition. First of all, a generalized kernel fuzzy C-means formula was applied in SAR image segmentation, and target shape in SAR image was extracted. Secondly, the triangle subdivision was obtained by means of Delaunay triangulation formula, and Laplace-Beltrami operator was discretized using cotangent weight scheme, and then heat kernel feature at every point was obtained by making use of the eigenvalues and eigenvectors of the discretized Laplace-Beltrami operator. Thirdly, heat kernel distance between two points was calculated by spectral distance formula, and then distance distribution was used to represent the heat kernel feature in the target shape of SAR image. Finally, L1 similarity criterion was adopted to measure the similarity of two SAR image, recognition result was obtained by comparing similarities of the whole SAR images. Experiments show that, compared with the classical Hu invariant feature which also has affine invariance in images, this method which is based on heat kernel feature shows both higher recognition rate for affine transformation SAR images and deformation SAR images. Consequently, SAR image target recognition method, which is based on the heat kernel feature, is a more effective SAR image recognition method.

     

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