High Resolution Remote Sensing Image Classification Using Multitask Joint Sparseand Low-rank Representation

Multitask learning is one of the machine learning methods, that trains multiple tasks in parallel using information sharing among the tasks. A high resolution remote sensing images classification model using multitask joint sparse and low-rank representation (MJSLR) is proposed in this paper. The model is a non-smooth convex optimization problem, which contains a convex smooth function and the two convex but non-smooth regularization terms. The accelerated proximal gradient method solves the optimization problem. An experiment is performed with UC Merced Land Use Dataset, with comparisons of accuracies between multitask learning and the single task learning. Experimental results show that the proposed method is competitive with Multiple Kernel Learning(MKL) and the Multitask Joint Sparse Representation(MJSR) methods, which demonstrates the effectiveness of the MJSLR method.