Automatic seagrass detection: A survey

© 2017 IEEE. Seagrass is an important component of the marine ecosystem and plays a vital role in preserving the water quality. The traditional approaches for sea grass identification are either manual or semi-automated, resulting in costlier, time consuming and tedious solutions. There has been an increasing interest in the automatic identification of seagrasses and this article provides a survey of automatic classification techniques that are based on machine learning, fuzzy synthetic evaluation model and maximum likelihood classifier along with their performance. The article classifies the existing approaches on the basis of image types (i.e. aerial, satellite, and underwater digital), outlines the current challenges and provides future research directions

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

APPLICATION OF REMOTE SENSING IN AQUATIC ECOSYSTEMS

I utilized state the art remote sensing and GIS (Geographical Information System) techniques to study large scale biological, physical and ecological processes of coastal, nearshore, and offshore waters of Lake Michigan and Lake Superior. These processes ranged from chlorophyll a and primary production time series analysies in Lake Michigan to coastal stamp sand threats on Buffalo Reef in Lake Superior. I used SeaWiFS (Sea-viewing Wide Field-of-view Sensor) satellite imagery to trace various biological, chemical and optical water properties of Lake Michigan during the past decade and to investigate the collapse of early spring primary production. Using spatial analysis techniques, I was able to connect these changes to some important biological processes of the lake (quagga mussels filtration). In a separate study on Lake Superior, using LiDAR (Light Detection and Ranging) and aerial photos, we examined natural coastal erosion in Grand Traverse Bay, Michigan, and discussed a variety of geological features that influence general sediment accumulation patterns and interactions with migrating tailings from legacy mining. These sediments are moving southwesterly towards Buffalo Reef, creating a threat to the lake trout and lake whitefish breeding ground

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

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

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Light detection and ranging (LiDAR) and multispectral studies of disturbed Lake Superior coastal environments

Due to its high spatial resolution and excellent water penetration, coastal light detection and ranging (LiDAR) coupled with multispectral imaging (MSS) has great promise for resolving shoreline features in the Great Lakes. Previous investigations in Lake Superior documented a metal-rich “halo” around the Keweenaw Peninsula, related to past copper mining practices. Grand Traverse Bay on the Keweenaw Peninsula provides an excellent Great Lakes example of global mine discharges into coastal environments. For more than a century, waste rock migrating from shoreline tailings piles has moved along extensive stretches of coast, damming stream outlets, intercepting wetlands and recreational beaches, suppressing benthic invertebrate communities, and threatening critical fish breeding grounds. In the bay, the magnitude of the discarded wastes literally “reset the shoreline” and provided an intriguing field experiment in coastal erosion and spreading environmental effects. Employing a combination of historic aerial photography and LiDAR, we estimate the time course and mass of tailings eroded into the bay and the amount of copper that contributed to the metal-rich halo. We also quantify underwater tailings spread across benthic substrates by using MSS imagery on spectral reflectance differences between tailings and natural sediment types, plus a depth-correction algorithm (Lyzenga Method). We show that the coastal detail from LiDAR and MSS opens up numerous applications for ecological, ecosystem, and geological investigations

A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data

[EN] Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can provide suitable data for mapping floating plastic, but most of the methods require visual interpretation and manual labeling. The main goals of this paper are to determine the suitability of deep learning algorithms for automatic floating plastic extraction from UAV orthophotos, testing the possibility of differentiating plastic types, and exploring the relationship between spatial resolution and detectable plastic size, in order to define a methodology for UAV surveys to map floating plastic. Two study areas and three datasets were used to train and validate the models. An end-to-end semantic segmentation algorithm based on U-Net architecture using the ResUNet50 provided the highest accuracy to map different plastic materials (F1-score: Oriented Polystyrene (OPS): 0.86; Nylon: 0.88; Polyethylene terephthalate (PET): 0.92; plastic (in general): 0.78), showing its ability to identify plastic types. The classification accuracy decreased with the decrease in spatial resolution, performing best on 4 mm resolution images for all kinds of plastic. The model provided reliable estimates of the area and volume of the plastics, which is crucial information for a cleaning campaign.S

Aerial Simultaneous Localization and Mapping Using Earth\u27s Magnetic Anomaly Field

Aerial magnetic navigation has been shown to be a viable GPS-alternative, but requires a prior-surveyed magnetic map. The miniaturization of atomic magnetometers extends their application to small aircraft at low altitudes where magnetic maps are especially inaccurate or unavailable. This research presents a simultaneous localization and mapping (SLAM) approach to constrain the drift of an inertial navigation system (INS) without the need for a magnetic map. The filter was demonstrated using real measurements on a professional survey flight, and on an AFIT unmanned aerial vehicle

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future