Distributed Deep Learning for Big Remote Sensing Data Processing on Apache Spark: Geological Remote Sensing Interpretation as a Case Study.

The advent of sensor technologies has led to the sheer amount volume of remote sensing data containing fruitful spatial and spectral information. Insights into Earth’s surface’s objects are gained with the help of remote sensing processing methods and techniques and are applied in various applications. Recently, deep-learning-based methods are widely used in remote sensing data processing due to their ability to mine relationships using multiple layers. However, the time spent by deep learning-based methods with numerous layers and large parameter sizes in processing remote sensing data with “big data” characteristics is unacceptable in real-time applications. Combining deep learning with distributed computing namely distributed deep learning, has become an emerging topic in deep learning-based remote sensing processing. This paper first surveys recent methods and open-source solutions of Apache Spark-based distributed deep learning. Then, the pros and cons of each distributed deep learning open-source solution in processing remote sensing data are summarized. Later, the geological remote sensing interpretation is chosen as the case study by implementing the online training of a deep learning-based interpretation model called D-AMSDFNet for geological environments on Apache Spark. Experiments on Landsat 8 and Sentinel 2 satellite images investigate the effectiveness of the proposed D-AMSDFNet, which also indicates the promising development of distributed deep learning in processing remote sensing data.