Abstract
Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods.
Highlights
Introduction and Distributed Spark FrameworksEveryday, space-borne and airborne sensors deliver a huge number of Earth observation data so that we can observe the whole Earth from different angles
We present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification
Space-borne and airborne sensors deliver a huge number of Earth observation data so that we can observe the whole Earth from different angles. This data is the main actor for big data in remote sensing and it can be used in several domains such as monitoring, climate change, agriculture, and urban planning
Summary
Space-borne and airborne sensors deliver a huge number of Earth observation data so that we can observe the whole Earth from different angles. This data is the main actor for big data in remote sensing and it can be used in several domains such as monitoring, climate change, agriculture, and urban planning. Big data is to determine its Vs: volume, velocity, variety and veracity These four Vs [1]. Images delivered by different sensors, whether active or passive, have become more and more accessible. Remote Sensing Data (BRSD), this denomination linking the two domains: big data and remote sensing. What we have to know is how these Vs are used in BRSD
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