As the network structures continue to innovate and evolve, significant achievements have been achieved in hyperspectral image super-resolution tasks. However, how to further explore the spectral domain potential from prior knowledge and channel-enhanced structures to achieve better performance has inspired the following two works: Firstly, to systematically compare prior knowledge of spectral with spatial domain for HSI-SR tasks, four transfer learning strategies are proposed. The superior performance of the Relevant Channel/Random Space (RCRS) strategy reveals the importance of spectral feature reconstruction in HSI-SR tasks. Meanwhile, an interesting phenomenon has been observed that even without training on real datasets, the model can already exhibit a core or even decent super-resolution capability based solely on prior knowledge of above four strategies. Secondly, a dual-branch channel network with complementary channel feature extraction (CCFE) and adjacent channel feature extraction (ACFE) module is designed for spectral feature enhancement, which demonstrate superior performance compared to state-of-the-art methods on six datasets. To conclude, the effectiveness of RCRS strategy with pseudo prior channel knowledge on seven dual-input and eight single-input networks, as well as superiority of the proposed channel-enhanced network indicate the importance of spectral properties for HSI-SR tasks.

中文翻译：

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高光谱超分辨率的谱域策略：迁移学习和通道增强网络


随着网络结构的不断创新和发展，高光谱图像超分辨率任务取得了重大成就。然而，如何进一步从先验知识和通道增强结构中挖掘谱域潜力以实现更好的性能启发了以下两项工作：首先，为了系统地比较 HSI-SR 任务的谱域先验知识和空间域知识，四种迁移学习提出了策略。相关通道/随机空间（RCRS）策略的优越性能揭示了光谱特征重建在HSI-SR任务中的重要性。同时，我们观察到一个有趣的现象，即使没有在真实数据集上进行训练，仅基于上述四种策略的先验知识，模型就已经可以表现出核心甚至不错的超分辨率能力。其次，设计了具有互补通道特征提取（CCFE）和相邻通道特征提取（ACFE）模块的双分支通道网络用于光谱特征增强，与六个数据集上的最先进方法相比，该网络表现出优越的性能。总之，具有伪先验信道知识的 RCRS 策略在七个双输入和八个单输入网络上的有效性，以及所提出的信道增强网络的优越性表明了频谱特性对于 HSI-SR 任务的重要性。