Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

High-speed multi-dimensional relative navigation for uncooperative space objects

This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation

Exploiting satellite SAR for archaeological prospection and heritage site protection

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian “Valley of the Kings” (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability