Given the high availability of data collected by different remote sensing instruments, the data fusion of multi-spectral and hyperspectral images (HSI) is an important topic in remote sensing. In particular, super-resolution as a data fusion application using spatial and spectral domains is highly investigated because its fused images is used to improve the classification and tracking objects accuracy. On the other hand, the huge amount of data obtained by remote sensing instruments represent a key concern in terms of data storage, management and pre-processing. This paper proposes a Big Data Cloud platform using Hadoop and Spark to store, manages, and process remote sensing data. Also, a study over the parameter \textit{chunk size} is presented to suggest the appropriate value for this parameter to download imagery data from Hadoop into a Spark application, based on the format of our data. We also developed an alternative approach based on Long Short Term Memory trained with different patch sizes for super-resolution image. This approach fuse hyperspectral and multispectral images. As a result, we obtain images with high-spatial and high-spectral resolution. The experimental results show that for a chunk size of 64k, an average of 3.5s was required to download data from Hadoop into a Spark application. The proposed model for super-resolution provides a structural similarity index of 0.98 and 0.907 for the used dataset.

翻译：鉴于不同遥感仪器采集数据的高度可用性，多光谱与高光谱图像（HSI）的数据融合是遥感领域的重要课题。其中，利用空间域与光谱域的数据融合应用——超分辨率技术备受关注，因其融合图像可提升目标分类与跟踪的精度。另一方面，遥感仪器获取的海量数据在存储、管理与预处理方面构成关键挑战。本文提出一种基于Hadoop与Spark的大数据云平台，用于存储、管理与处理遥感数据。同时，本研究针对参数“分块大小”（chunk size）展开分析，基于数据格式建议从Hadoop向Spark应用下载影像数据时的适宜取值。我们进一步提出一种基于长短期记忆网络（LSTM）的替代方法，采用不同块大小进行超分辨率训练，实现高光谱与多光谱图像的融合，最终获得高空间分辨率与高光谱分辨率的图像。实验结果表明，当分块大小为64k时，从Hadoop向Spark应用下载数据平均耗时3.5秒。所提出的超分辨率模型在所用数据集上的结构相似性指数达到0.98与0.907。