HySpecNet-11k：用于基准测试基于学习的高光谱图像压缩方法的大规模高光谱数据集
HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods
AI总结：
本文提出大规模高光谱基准数据集HySpecNet-11k，含11,483个图像块，用于基准测试基于学习的压缩方法，并评估多种卷积自编码器架构。
摘要：
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极大关注。此类方法在训练期间需要大量高光谱图像来优化所有参数并达到高压缩性能。然而，现有的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。为解决这一问题，本文提出了HySpecNet-11k，这是一个由11,483个不重叠图像块组成的大规模高光谱基准数据集。每个图像块是128×128像素的一部分，具有224个光谱波段，地面采样距离为30米。我们利用HySpecNet-11k，通过关注各种1D、2D和3D卷积自编码器架构，对当前基于学习的高光谱图像压缩的最先进技术进行基准测试。尽管如此，HySpecNet-11k可用于高光谱图像分析框架中的任何无监督学习任务。该数据集、我们的代码和预训练权重可在https://hyspecnet.rsim.berlin公开获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
