基于自适应参数支持向量机的高光谱遥感图像小目标检测

Small Target Detection in Hyperspectral Remote Sensing Image Based on Adaptive Parameter SVM

针对高光谱遥感图像的小目标检测问题,提出了一种基于自适应参数支持向量机(SVM)的检测方法。采用主成分分析(PCA)法对高光谱遥感图像进行降维,降低数据冗余度;之后通过无监督检测方法对小目标进行快速、粗糙定位,并将该定位结果作为后验信息输入到SVM中;依据后验信息与核空间散度准则自适应确定SVM中核函数的参数,并使用SVM在核空间中寻找分离目标和背景的最佳超平面;利用该超平面将像元重新分类为背景和目标,并且迭代上述操作,得到精确且稳定的目标检测结果。大量实验结果表明,与经典RX方法、核RX方法、支持向量数据描述(SVDD)方法相比,该方法可以更有效、精确地检测出高光谱遥感图像中的小目标。

As for the problem of small target detection in hyperspectral remote sensing image, a detection method based on adaptive parameter support vector machine （SVM） is proposed. The low dimensional information of the hyperspectral image is obtained using the method of principal component analysis （PCA） and the redundancy of data is reduced. Then, small targets are positioned fast and roughly by an unsupervised detection method, and the posterior information of SVM is got by the position result. The kernel parameter of SVM is determined adaptively based on the posterior information and the criteria of divergence in the kernel space. The best hyperplane in the kernel space for the segmentation of targets and background is found by the SVM. Pixels are separated to targets and background by the best hyperplane. The accurate and stable target detection result is obtained by iteration. A large number of experimental results show that, compared to the existing methods such as RX method, kernel RX method and support vector data description （SVDD） method, the proposed method is more effective to detect small targets accurately in the hyperspectral remote sensing image.