Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, offering potential for performance improvements in multi-task learning. However, differences between tasks can make it challenging to balance their performances. To address this challenge, this paper proposes a multi-task multi-objective evolutionary network (DMOEAD) for joint learning of HC and HP. A multi-task sufficiency-and-diversity sampling method is designed to unify the heterogeneity of sample construction between two types of tasks. Two types of task-specific networks are constructed to decompose high-frequency information. Further, a collaborative learning module is designed to dynamically learn complementary high-frequency information from another task in different layers. To be compatible with the optimization direction of two types of tasks, multi-task optimization is realized using a deep multi-objective evolutionary algorithm (DMEO). In the DMEO, the set of parameters of the DMOEAD is regarded as an individual. A deep mutation operator is designed and used for network optimization, which accelerates large-scale network parameter searching. The DMEO can coordinate the differences between multiple tasks and provide a set of Pareto network parameter solutions. Finally, the experimental results demonstrate that the proposed method can significantly enhance the performance of both pansharpening and classification tasks.

中文翻译：

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用于高光谱图像分类和全色锐化的多任务多目标进化网络

多任务学习在联合视觉感知任务中被广泛使用并且表现良好。高光谱全色锐化 (HP) 和高光谱分类 (HC) 任务提取高频信息以增强边缘并对样本进行分类，从而为多任务学习的性能改进提供了潜力。然而，任务之间的差异可能会使平衡其性能变得具有挑战性。为了应对这一挑战，本文提出了一种用于 HC 和 HP 联合学习的多任务多目标进化网络（DMOEAD）。设计了多任务充分性和多样性采样方法来统一两类任务之间样本构建的异质性。构建了两种类型的任务特定网络来分解高频信息。此外，协作学习模块被设计为动态地从不同层的另一个任务中学习互补的高频信息。为了兼容两类任务的优化方向，采用深度多目标进化算法（DMEO）实现多任务优化。在DMEO中，DMOEAD的参数集被视为一个个体。设计了深度变异算子并用于网络优化，加速了大规模网络参数搜索。 DMEO可以协调多个任务之间的差异，并提供一套Pareto网络参数解决方案。最后，实验结果表明，所提出的方法可以显着提高全色锐化和分类任务的性能。