Artificial Intelligence Algorithm for Remote Sensing Imagery Processing

Dear Colleagues,

During the last decades, significant efforts have been made in the Remote Sensing field in order to obtain rich and accurate information about the earth’s surface. The impressive advances in computer technology, in terms of hardware devices and software desing, have enabled the launch of multiple earth observation missions, which are currently collecting huge amounts of data daily. These raw data captures the matter-energy interactions at the earth’s surface and are characterised by their great variety in terms of typology (e.g. lidar and radar data or optical and thermal imaging), acquisition platforms (e.g. unnamed aerial vehicles or UAV, traditional aerial platforms and satelites) and spatial, spectral and temporal resolutions (from high to low spatial resolution data, from single band pachromatic images to hyperspectral images with hundreds of spectral channels, and revisit times for the same observation area from hours to days). The opportunities of using remote sensing data to contribute to economic and social activities is highly attractive, as they collect rich information over large spatial areas, which enables the detailed characterization of natural features, different materials and physical objects on the ground. Indeed, the current literature on the use and exploitation of these data has proved that they are truly useful in different decision-making tasks, such as precision agriculture, natural resource management, urban planning, risk prevention, disasters management, national defense, and homeland security, among many other application areas. 

However, the raw data obtained by remote sensors must be properly processed in order to exploit the information contained in them, refining the data to the end-user level. In this sense, this data processing has to deal with several challenges and limitations, such as high data complexity, noise data due to sensor limitations or uncontrolled atmospheric changes, low spatial resolutions, spectral mixtures, redundancies and correlations between spectral bands, lack of labelled samples, cloud occlusions, high data dimensionality, high intra-class variability and inter-class similarity… To face these issues, the implementation of new, more powerful processing tools is absolutely mandatory, which must be able to  extract the relevant information contained into remote sensing data in a reliable and efficient way. 

In this regard, artificial intelligence (AI) techniques have had significant successes in multiple fields related to data processing, such as speech recognition and computer vision. These methods provide interesting procedures to automaticaly process large amounts of data and conduct data-drive decisions in an accurate way. Moreover, the increasing capabilities of computer systems have promoted a great evolution of these algorithms, from traditional pattern recognition methods to complex machine and task-driven deep learning models, which are achieving unprecedented results. In particular, many AI-based algorithms are achieving dramatic improvements in many remote sensing analysis, such as unmixing, data classification, object/target or anomaly/change detection, data super-resolution, data fusion, cloud removal, denoising, spectral reduction… However, the implementation of these algorithms to remote data processing must address the characteristics, challenges and limitations imposed by this kind of data, and therefore remains a challenging task. 

This Special Issue invites manuscripts that present new AI approaches or improved AI-based algorithms for processing the information contained into remote sensing data. As this is a broad area, there are no constraints regarding the field of application. In this sense, the aim of this special issue will focus on presenting the current state of AI methods for the analysis of remote sensing data in several fields of application.

Dr. Mercedes E. Paoletti
Dr. Juan M. Haut
Guest Editors

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