Dolphin-inspired target detection for sonar and radar

Gas bubbles in the ocean are produced by breaking waves, rainfall, methane seeps, exsolution, and a range of biological processes including decomposition, photosynthesis, respiration and digestion. However one biological process that produces particularly dense clouds of large bubbles, is bubble netting. This is practiced by several species of cetacean. Given their propensity to use acoustics, and the powerful acoustical attenuation and scattering that bubbles can cause, the relationship between sound and bubble nets is intriguing. It has been postulated that humpback whales produce ‘walls of sound’ at audio frequencies in their bubble nets, trapping prey. Dolphins, on the other hand, use high frequency acoustics for echolocation. This begs the question of whether, in producing bubble nets, they are generating echolocation clutter that potentially helps prey avoid detection (as their bubble nets would do with man-made sonar), or whether they have developed sonar techniques to detect prey within such bubble nets and distinguish it from clutter. Possible sonar schemes that could detect targets in bubble clouds are proposed, and shown to work both in the laboratory and at sea. Following this, similar radar schemes are proposed for the detection of buried explosives and catastrophe victims, and successful laboratory tests are undertaken

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

A ship detector applying Principal Component Analysis to the polarimetric Notch Filter

Ship detection using polarimetric synthetic aperture radar (PolSAR) data has attracted a lot of attention in recent years. Polarimetry can provide information regarding the scattering mechanisms of targets, which helps discriminate between ships and sea clutter. This enhancement is particularly valuable when we aim at detecting smaller vessels in rough sea states. This work exploits a ship detector called the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF), and it is aimed at improving its performance especially when less polarimetric images are available (e.g., dual-polarimetric data). The idea is to design a new polarimetric feature vector containing more features that are renowned to allow separation between ships and sea clutter. Then, a Principal Component Analysis (PCA) is further used to reduce the dimensionality of the new feature space. Experiments on four real Sentinel-1 datasets are carried out to demonstrate the validity of the proposed method and compare it against other ship detectors. Analyses of the experimental results show that the proposed algorithm can not only reduce the false alarms significantly, but also enhance the target-to-clutter ratio (TCR) so that it can more effectively detect weaker ships

A facility to Search for Hidden Particles (SHiP) at the CERN SPS

A new general purpose fixed target facility is proposed at the CERN SPS accelerator which is aimed at exploring the domain of hidden particles and make measurements with tau neutrinos. Hidden particles are predicted by a large number of models beyond the Standard Model. The high intensity of the SPS 400~GeV beam allows probing a wide variety of models containing light long-lived exotic particles with masses below ${\cal O}$(10)~GeV/c$^2$, including very weakly interacting low-energy SUSY states. The experimental programme of the proposed facility is capable of being extended in the future, e.g. to include direct searches for Dark Matter and Lepton Flavour Violation.Comment: Technical Proposa

The use of lasers for hydrographic studies

The utilization of remote laser sensors in water pollution detection and identification, coastal environmental monitoring, and bathymetric depth sounding, is discussed. q

Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing

We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionaly, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS

Investigation of passive atmospheric sounding using millimeter and submillimeter wavelength channels

Activities within the period from July 1, 1992 through December 31, 1992 by Georgia Tech researchers in millimeter and submillimeter wavelength tropospheric remote sensing have been centered around the calibration of the Millimeter-wave Imaging Radiometer (MIR), preliminary flight data analysis, and preparation for TOGA/COARE. The MIR instrument is a joint project between NASA/GSFC and Georgia Tech. In the current configuration, the MIR has channels at 90, 150, 183(+/-1,3,7), and 220 GHz. Provisions for three additional channels at 325(+/-1,3) and 8 GHz have been made, and a 325-GHz receiver is currently being built by the ZAX Millimeter Wave Corporation for use in the MIR. Past Georgia Tech contributions to the MIR and its related scientific uses have included basic system design studies, performance analyses, and circuit and radiometric load design, in-flight software, and post-flight data display software. The combination of the above millimeter wave and submillimeter wave channels aboard a single well-calibrated instrument will provide unique radiometric data for radiative transfer and cloud and water vapor retrieval studies. A paper by the PI discussing the potential benefits of passive millimeter and submillimeter wave observations for cloud, water vapor and precipitation measurements has recently been published, and is included as an appendix

A notch filter for ship detection with polarimetric SAR data

Ship detection with Synthetic Aperture Radar (SAR) is a major topic for the security and monitoring of maritime areas. One of the advantages of using SAR lay in its capability to acquire useful images with any-weather conditions and at night time. Specifically, this paper proposes a new methodology exploiting polarimetric acquisitions (dual- and quad-polarimetric). The methodology adopted for the detector algorithm was introduced by the author and performs a perturbation analysis in space of polarimetric targets checking for coherence between the target to detect and its perturbed version on the data. In the present work, this methodology is optimized for detection of marine features. In the end, the algorithm can be considered to be a negative (notch) filter focused on sea. Consequently, all the features which have a polarimetric behavior different from the sea are detected (i.e. ships, icebergs, buoys, etc). Moreover, a dual polarimetric version of the detector is designed, to be exploited in the circumstances where quad polarimetric data cannot be acquired. The detector was tested with TerraSAR-X quad polarimetric data showing significant agreement with the available ground truth. Moreover, the theoretical performances of the detector are tested with Monte Carlo simulations in order to extract the probabilities of detection and false alarm. An important result is that the detector is, up to some extend, independent of the sea conditions