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

PRECONDITIONING AND THE APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY MOVING TARGETS IN SAR IMAGERY

Synthetic Aperture Radar (SAR) is a principle that uses transmitted pulses that store and combine scene echoes to build an image that represents the scene reflectivity. SAR systems can be found on a wide variety of platforms to include satellites, aircraft, and more recently, unmanned platforms like the Global Hawk unmanned aerial vehicle. The next step is to process, analyze and classify the SAR data. The use of a convolutional neural network (CNN) to analyze SAR imagery is a viable method to achieve Automatic Target Recognition (ATR) in military applications. The CNN is an artificial neural network that uses convolutional layers to detect certain features in an image. These features correspond to a target of interest and train the CNN to recognize and classify future images. Moving targets present a major challenge to current SAR ATR methods due to the “smearing” effect in the image. Past research has shown that the combination of autofocus techniques and proper training with moving targets improves the accuracy of the CNN at target recognition. The current research includes improvement of the CNN algorithm and preconditioning techniques, as well as a deeper analysis of moving targets with complex motion such as changes to roll, pitch or yaw. The CNN algorithm was developed and verified using computer simulation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Camera methods for the assessment of coastal biodiversity in low visibility environments

Coastal marine environments are important ecological, economic and social areas providing valuable services such as coastal protection, areas of recreation and tourism, fishing, climate regulation, biotic materials and biofuels. Marine renewable energy developments in the coastal environment are becoming a key objective for many countries globally. Assessing and monitoring the impacts of these developments on features, such as coastal biodiversity, becomes a difficult prospect in these environments due to the complexity of marine process at the locations in which these developments are targeted. This thesis explores the main challenges faced when assessing biodiversity in dynamic coastal environments, in particular those susceptible to high levels of turbidity. Various underwater camera techniques were trialled in reduced visibility environments including baited remote underwater video (BRUV), drop-down video and hydroacoustic methods. This research successfully refined BRUV guidelines in the North-East Atlantic region and identified key methodological and environmental factors influencing data collected BRUV deployments. Key findings included mackerel as the recommended bait type in this region and highlighting the importance of collecting consistent metadata when using these methods. In areas of high turbidity, clear liquid optical chambers (CLOCs) were successfully used to enhance the quality of information gathered using underwater cameras when monitoring benthic fauna and fish assemblages. CLOCs were applied to both conventional BRUV camera systems and benthic drop-down camera systems. Improvements included image quality, species and habitat level identification, and taxonomic richness. Evaluations of the ARIS 3000 imaging sonar and its capability of visualising distinguishing identifying features in low visibility environments for motile fauna showed mixed results with morphologically distinct species such as elasmobranchs much clearer in the footage compared to individuals belonging to finfish families. A combined approach of optical and hydroacoustic camera methods may be most suitable for adequately assessing coastal biodiversity in low visibility environments

Shock Wave Dynamics of Novel Aluminized Detonations and Empirical Model for Temperature Evolution from Post-Detonation Combustion Fireballs

This research characterizes the blast wave and temperature evolution of an explosion fireball in order to improve the classification of aluminized conventional munitions based on a single explosive type such as RDX. A drag model fit to data shows initial shock velocities of 1.6-2.8 km/s and maximum fireball radii ranging from 4.3-5.8 m with most of the radii reached by 50 ms upon detonation. The Sedov-Taylor point blast model is fitted to data where a constant release (s=1) of energy upon detonation suggests shock energies of 0.5-8.9 MJ with blast dimensionalities indicative of the spherical geometry (n3) observed in visible imagery. An inverse correlation exists between blast wave energy and overall aluminum content in the test articles. Using a radiative cooling term and a secondary combustion term, a physics-based empirical model is able to reduce 82 data points to five fit parameters to describe post-detonation combustion fireballs. The fit-derived heat of combustion has a 96% correlation with the calculated heat of combustion but has a slope of 0.49 suggesting that only half of the theoretical heat of combustion is realized. Initial temperature is not a good discriminator of detonation events but heat of combustion holds promise as a potential variable for event classification

Versatile image-based measurements of granular flows and water wave propagation in experiments of tsunamis generated by landslides

Landslides falling into water bodies can generate destructive waves, which can be classified as tsunamis. An experimental facility to study this phenomenon has been set up. It consists of a landslidegenerator releasing gravel at high-speed into a wave basin. A non-intrusive system has been designed ad-hocto be able to measure the high velocity and the geometry of the landslide as well as the generated waves characteristics. The measurement system employs the treatment of images captured by a high-speed camera which records the launched granular material illuminated by a laser sheet. A grid of laser sheets marks thebasin water surface. The water has been filled by a small amount of kaolin to properly reflect the laser lightat water surface. Thus, by filming with high definition cameras the perturbed water surface and successively processing the resulting images, it has been possible to measure the generated waves. The measurement framework employs a versatile camera calibration technique which allows accurate measurements in presence of: (1) high lens distortions; (2) pronounced non-parallelism condition between camera sensor and plane of measurement coincident with the laser sheet. The maximum resolution of the measurement tool is0.01 mm, while the maximum uncertainty due to systematic error has been estimated to be 15% for theworst-case scenario. This work improves the suitability of image-based measuring systems in granular flows and free surface hydraulics experimentsThis work was funded by GITS and the 3 years’ national project DEBRIS FLOW (CGL 2009-13039) ofthe Spanish Ministry of Education. Francesco Bregoli has been supported by the 4-years grant FPU2009-3766 of the SpanishMinistry of Education. Authors want to thank Dr. Cecilia Caldini (IDOM Consulting, Barcelona) for the 3D reconstruction ofthe laboratory setup.Peer ReviewedPostprint (published version

California coast nearshore processes study

There are no author-identified significant results in this report

Aeronautics and space report of the President, 1980 activities

The year's achievements in the areas of communication, Earth resources, environment, space sciences, transportation, and space energy are summarized and current and planned activities in these areas at the various departments and agencies of the Federal Government are summarized. Tables show U.S. and world spacecraft records, spacecraft launchings for 1980, and scientific payload anf probes launched 1975-1980. Budget data are included

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas