The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspectral images to be used during training to optimize all parameters and reach a high compression performance. However, existing hyperspectral datasets are not sufficient to train and evaluate learning-based compression methods, which hinders the research in this field. To address this problem, in this paper we present HySpecNet-11k that is a large-scale hyperspectral benchmark dataset made up of 11,483 nonoverlapping image patches. Each patch is a portion of 128 $\times$ 128 pixels with 224 spectral bands and a ground sample distance of 30 m. We exploit HySpecNet-11k to benchmark the current state of the art in learning-based hyperspectral image compression by focussing our attention on various 1D, 2D and 3D convolutional autoencoder architectures. Nevertheless, HySpecNet-11k can be used for any unsupervised learning task in the framework of hyperspectral image analysis. The dataset, our code and the pre-trained weights are publicly available at https://hyspecnet.rsim.berlin .

翻译：近年来，基于学习的高光谱图像压缩方法的发展在遥感领域引起了广泛关注。此类方法在训练过程中需要大量高光谱图像来优化所有参数并达到较高的压缩性能。然而，现有高光谱数据集不足以训练和评估基于学习的压缩方法，这阻碍了该领域的研究进展。为解决这一问题，本文提出HySpecNet-11k，一个由11483个非重叠图像块组成的大规模高光谱基准数据集。每个图像块为128×128像素，包含224个光谱波段，地面采样距离为30米。我们利用HySpecNet-11k对当前基于学习的高光谱图像压缩技术进行了基准测试，重点研究了多种一维、二维和三维卷积自编码器架构。此外，HySpecNet-11k还可用于高光谱图像分析框架下的任何无监督学习任务。该数据集、相关代码及预训练权重已在https://hyspecnet.rsim.berlin 公开提供。