基于多任务联合稀疏和低秩表示的高分辨率遥感图像分类

多任务学习（multitask learning，MTL）是一种利用多个任务间共享信息并行学习以提高模型泛化性能的机器学习方法，研究表明该方法可以提升高分辨率遥感图像的分类精度。提出一种基于多任务联合稀疏和低秩表示（multitask joint sparse and low-rank representation，MJSLR）的高分辨率遥感图像分类模型，并采用加速近似梯度法求解凸的光滑函数和非光滑约束的组合优化问题。实验对比分析了多任务和单任务的学习模型，并比较了MJSLR、多核学习方法和多任务联合稀疏表达方法的图像分类准确率，结果表明多任务学习模型能够获得优于单任务学习模型的分类精度，而且融合低秩约束能够一定程度上提高多任务分类模型的精度。

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.