Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this high-dimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational complexity and energy requirements bring some challenges. Adopting low-power consumption architectures and deep learning (DL) approaches has to provide acceptable computing capabilities without reducing accuracy demand. However, most DL architectures employ single-precision (FP32) to train models, and some big DL architectures will have a limitation on memory and computation resources. This can negatively affect the network learning process. This letter leads these challenges by using mixed precision into DL architectures for HSI classification to speed up the training process and reduce the memory consumption/access. Proposed models are evaluated on four widely used data sets. Also, low and high-power consumption devices are compared, considering NVIDIA Jetson Xavier and Titan RTX GPUs, to evaluate the proposal viability in on-board processing devices. Obtained results demonstrate the efficiency and effectiveness of these models within HSI classification task for both devices. Source codes: https://github.com/mhaut/CNN-MP-HSI.

高光谱图像（HSI）记录了不同波长通道的场景，提供了详细的空间和光谱信息。在许多实际应用中，如何存储和处理这种高维数据起着至关重要的作用，其中分类技术已成为出色的处理工具。然而，它们的高计算复杂性和能量需求带来了一些挑战。采用低功耗架构和深度学习（DL）方法必须在不降低准确性要求的情况下提供可接受的计算能力。但是，大多数DL架构都采用单精度（FP32）来训练模型，并且某些大型DL架构会限制内存和计算资源。这会对网络学习过程产生负面影响。这封信通过将混合精度用于HSI分类的DL架构中来加快训练过程并减少内存消耗/访问，从而应对了这些挑战。建议的模型是根据四个广泛使用的数据集进行评估的。此外，考虑了NVIDIA Jetson Xavier和Titan RTX GPU，对低功耗和高功耗设备进行了比较，以评估提议在机载处理设备中的可行性。获得的结果证明了在两种设备的HSI分类任务中这些模型的效率和有效性。源代码：https://github.com/mhaut/CNN-MP-HSI。考虑使用NVIDIA Jetson Xavier和Titan RTX GPU，以评估提案在车载处理设备中的可行性。获得的结果证明了在两种设备的HSI分类任务中这些模型的效率和有效性。源代码：https://github.com/mhaut/CNN-MP-HSI。考虑使用NVIDIA Jetson Xavier和Titan RTX GPU，以评估提案在车载处理设备中的可行性。获得的结果证明了在两种设备的HSI分类任务中这些模型的效率和有效性。源代码：https://github.com/mhaut/CNN-MP-HSI。