## 2 Background on Technologies for RSBD Processing and Related Works
### Related Works  
Many investigations have been focused on diverse architectures to solve RS data processing issues. These studies aimed to customize parallel computing by integrating the hardware into the capacity [13] to store and process RSBD inside distributed clusters, such as the Hadoop [14], improve algorithms and the processing patterns, and manage RS data streaming. We cite other studies that are particularly related to the current approach. Regarding works that processed RSBD in a Hadoop platform, we can mention the paper by [PERSON] et al. [15], who proposed a parallel and suitable computing framework Hadoop for various service-oriented science applications. The results showed that this parallel programming paradigm efficiently processes the satellite data. Besides, it can be exploited for deriving higher-level data products from random RS systems. [PERSON] et al. [14] proposed a Hadoop-based framework to manage and process the RSBD in a distributed and parallel way. RS data can be directly raised from other data platforms into the HDFS. The experiment result indicates that the proposed framework can optimize the execution time when dealing with a massive RS data volume. [PERSON] et al. [16] came up with an in-memory computing framework to address RS processing. Thus, Spark is used to process in-streaming RS data. Data loaded into the memory in the first iteration on the Spark-based platform can be reused in subsequent iterations. The experiments demonstrated that the Spark-based platform's time cost is far less than the MR platform. It is significant to reference two recent software allowing distributed satellite image processing using Hadoop and Spark tools. In [17], an innovative parallel RSBD processing framework was developed called ScienceEarth, which stores, manages, indexes, and queries satellite images in a distributed system with high feasibility. [PERSON] et al. [18] also anticipated a Spark-based RSBD framework for image processing. The method integrates Landsat raster into HDFS, then MR maps, merges, and finally loads the looked-for tiles. The experimental results demonstrated fast and efficient processing. It is also noteworthy to mention the applications focused on RSBD inside a cloud platform. [PERSON] et al. [19] presented software-generating products using multisource RS data and crossways distributed computers in a cloud environment to reward the low production efficiency, fewer types, and simple services of the existing system. The program uses the \"master-slave\" paradigm. Thus, the proposed cloud-based RS production system manages massive RS data, and various products are generated. Some appropriate methods that focus on novel architectures for RS processing are explained as follows: [PERSON] [20] developed Java-based application software to collect, process, and visualize numerous environmental data acquired from the EUMETSAT datacenter. [PERSON] et al. [21] also proposed software as an extract-transform-load tool for satellite data pre-processing that allows effective RSBD integration. Thus, the developed software layer gathers data unceasingly and eliminates about 86% of the unemployed files. Many studies have also focused on satellite High Spectral Image (HSI) processing. The analyses of HSI are very challenging and require huge computer capacities and enhanced algorithms [22]. In [23], the authors developed an intelligent algorithm to refine HSI using the super-resolution fusing method. The developed algorithm is based on recent image processing techniques, such as Hybrid Color Mapping (HCM), the Plug-and-Play algorithm, etc. Some promising results were shown after the comparison and validation stages. In [24], a new benchmarking framework for panchromatic algorithms was defined. After pre-processing the HIS, some talented results showed the data quality assessment. In [25], a new algorithm was proposed to process HSI to detect pixel anomalies. This method is based on unmixing, and low-rank decomposition approaches.