  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.
