逐层特征选择的多层部件模型用于遥感图像飞机目标检测

Multi-layer Feature Selection Based Hierarchal Component Model for Aero-plane Detection on Remote Sensing Image

提出了一种基于逐层特征选择的多层部件模型目标检测算法(multi-layer feature selection based hierarchal component model,MFSHCM),用于遥感图像飞机目标检测。通过提取目标多特征并结合局部判别式模型的建模方法,首先将提取的目标多种特征采用多核学习的方法经过核函数变换后再进行组合,提高了目标描述的准确性;其次考虑到目标自身固有的结构特性,特别是层次结构关系,引入分层的思想,构造目标的分层结构特征,并通过分层特征选择有效地降低了特征计算的复杂度;最后将MKL多特征和分层结构相结合,利用LSVM学习和推理,提出了基于逐层特征选择的多层部件模型目标检测算法。实验中将该算法在收集的十大机场真实场景数据上进行测试,验证了算法的有效性。

In this paper,we propose a multiple feature based hierarchal component model for aeroplane detection on remote sensing image.In order to take the use of multiple features together with Part-based Model approach,we propose the new algorithm：first,in order to give a more clear description of the target,MKL learning method is used to combine the multiple feature extracted from the target through a liner combination procedure,then,because the object itself has structure features,we build a latent hierarchical structure model（LHSM）,at last,we combine the MKL and LHS together to form the algorithm proposed in the beginning.We also test the effects of the new algorithm by collecting the remote sensing images from ten international airports;the result shows that the new approach is worthwhile.