提出了一种基于L1范数的二维局部保留映射(two-dimensional locality preserving projections based on L1-norm,2DLPP-L1)特征提取方法。与传统的基于L2范数的二维局部保留映射(2DLPP)相比,所提方法有两个优点。首先,由于L1范数对噪声不敏感,因此它具有更强的抗噪声能力;其次,它不需要进行特征值分解。在两个人脸数据库和一个手写数字数据集上的实验结果表明,当训练集中有噪声时,所提的2DLPP-L1能够取得优于传统2DLPP的分类性能。
Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional （3D） satellites dataset named BUAA Satellite Image Dataset （BUAA-SID 1.0） to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections （KLPP）. To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k-nearest neighbor （kNN） is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.
