Multi-source imagery fusion using deep learning in a cloud computing platform

TL;DR

This paper presents a cloud-based big data platform using Hadoop and Spark for remote sensing data management and introduces a deep learning approach with LSTM for hyperspectral and multispectral image super-resolution, achieving high structural similarity.

Contribution

It proposes an integrated cloud platform for remote sensing data processing and a novel LSTM-based super-resolution method for multi-source imagery fusion.

Findings

Efficient data download with 64k chunk size (3.5s average)

High structural similarity index (0.98 and 0.907) for super-resolution images

Effective fusion of hyperspectral and multispectral images for enhanced resolution

Abstract

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.