LAUSR

Considering the multiple observations from satellites with different types of sensors, the available object data are regarded as different combinations of multisource heterogeneous data. To achieve accurate object recognition, the dimension reduction (DR) technology is important to capture the low-dimensional discriminative representation containing complementary information from multisource data, while off-the-shelf DR methods can only handle input represented as vector or homogeneous tensors and fail to deal with multisource data represented as heterogeneous tensors. In addition, the existing DR methods are classifier-independent, making it difficult to ensure effective recognition under a specific classifier. To solve these problems, the DR method for Heterogeneous Tensors based on Graph-based and Classifier-oriented Embedding (HTGCE) method is proposed to learn the low-dimensional representation of multisource data using different combinations of samples. First, self-learning adjacency matrices are constructed to capture the local structure of different combinations of multisource data autonomously. Then, unlike the classifier-independent discriminant term used in existing DR methods, a classifier-oriented discriminant term is constructed to enhance the specific classifier-based recognition results. Furthermore, the reconstruction error minimization term is created to enable DR results to inherit the main information of the original data. Moreover, an adaptive weight factor is built to balance the importance of different sources for object recognition. Finally, an alternative optimization strategy is presented to solve the optimization problem of HTGCE. Using the multiresolution multiangle optical dataset and the paired optical and SAR dataset, the experimental results demonstrate that the HTGCE outperforms typical vector- and tensor-based DR methods in terms of recognition accuracy.