基于热核特征的SAR图像地物识别方法

采用了一种空间敏感度特征包(spatially sensitive bags of feature, SS BOF)来实现合成孔径雷达（synthetic aperture radar，SAR）图像的地物识别。首先采用推广的核模糊C 均值方法分割SAR图像，提取SAR图像目标图形；采用Harris角点检测子提取角点，接着对目标图形进行Delaunay三角剖分；采用cotangent weight方法对三角剖分图赋值，进而求得离散化Laplace Beltrami算子的特征值、特征向量，并计算SS BOF，进而对地物目标进行识别，其识别方法采用比L1相似准则效果更好的相关系数法；最后与热核迹等热核不变量特征以及Hu不变矩特征进行对比。实验表明：空间敏感度热核特征的识别率高于热核不变量的识别率，并与经典的Hu不变矩特征比较，识别率有所提高。

Methods of SAR image terrain recognition based on heat kernel features

A spatially sensitive bags of feature （SS-BOF） is introduced which is used for terrain recognition of synthetic aperture radar (SAR) images. Firstly，a generalized nuclear fuzzy C Means method is used to segment the SAR images, then the target shape of each SAR image is extracted. Secondly, its corners are extracted by using Harris corner way, then the target graph is triangulated by Delaunay triangulation. Thirdly, the cotangent weight is assigned to the triangulation, then the eigenvalues and eigenvectors of the discretized Laplace Beltrami operator and SS BOF can be calculated, then objects are identified, and correlation coefficient means is adopted for the recognition method whose result is better than L-1 similar criterion. Finally, the SS BOF can be contrasted with heat kernel trace and other heat kernel invariant features, also Hu invariant moments. Experimental results show that the recognition rate of spatially sensitive heat kernel feature SS-BOF is higher than heat kernel invariant features, and compared with the classical Hu invariant moments, the recognition rate is increased.