2,626 research outputs found
Deep learning for Plankton and Coral Classification
Oceans are the essential lifeblood of the Earth: they provide over 70% of the
oxygen and over 97% of the water. Plankton and corals are two of the most
fundamental components of ocean ecosystems, the former due to their function at
many levels of the oceans food chain, the latter because they provide spawning
and nursery grounds to many fish populations. Studying and monitoring plankton
distribution and coral reefs is vital for environment protection. In the last
years there has been a massive proliferation of digital imagery for the
monitoring of underwater ecosystems and much research is concentrated on the
automated recognition of plankton and corals. In this paper, we present a study
about an automated system for monitoring of underwater ecosystems. The system
here proposed is based on the fusion of different deep learning methods. We
study how to create an ensemble based of different CNN models, fine tuned on
several datasets with the aim of exploiting their diversity. The aim of our
study is to experiment the possibility of fine-tuning pretrained CNN for
underwater imagery analysis, the opportunity of using different datasets for
pretraining models, the possibility to design an ensemble using the same
architecture with small variations in the training procedure. The experimental
results are very encouraging, our experiments performed on 5 well-knowns
datasets (3 plankton and 2 coral datasets) show that the fusion of such
different CNN models in a heterogeneous ensemble grants a substantial
performance improvement with respect to other state-of-the-art approaches in
all the tested problems. One of the main contributions of this work is a wide
experimental evaluation of famous CNN architectures to report performance of
both single CNN and ensemble of CNNs in different problems. Moreover, we show
how to create an ensemble which improves the performance of the best single
model
The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales
The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process
Applications of Machine Learning in Chemical and Biological Oceanography
Machine learning (ML) refers to computer algorithms that predict a meaningful
output or categorize complex systems based on a large amount of data. ML is
applied in various areas including natural science, engineering, space
exploration, and even gaming development. This review focuses on the use of
machine learning in the field of chemical and biological oceanography. In the
prediction of global fixed nitrogen levels, partial carbon dioxide pressure,
and other chemical properties, the application of ML is a promising tool.
Machine learning is also utilized in the field of biological oceanography to
detect planktonic forms from various images (i.e., microscopy, FlowCAM, and
video recorders), spectrometers, and other signal processing techniques.
Moreover, ML successfully classified the mammals using their acoustics,
detecting endangered mammalian and fish species in a specific environment. Most
importantly, using environmental data, the ML proved to be an effective method
for predicting hypoxic conditions and harmful algal bloom events, an essential
measurement in terms of environmental monitoring. Furthermore, machine learning
was used to construct a number of databases for various species that will be
useful to other researchers, and the creation of new algorithms will help the
marine research community better comprehend the chemistry and biology of the
ocean.Comment: 58 Pages, 5 Figure
A multidisciplinary approach for generating globally consistent data on mesophotic, deep-pelagic, and bathyal biological communities
Approaches to measuring marine biological parameters remain almost as diverse as the researchers who measure them. However, understanding the patterns of diversity in ocean life over different temporal and geographic scales requires consistent data and information on the potential environmental drivers. As a group of marine scientists from different disciplines, we suggest a formalized, consistent framework of 20 biological, chemical, physical, and socioeconomic parameters that we consider the most important for describing environmental and biological variability. We call our proposed framework the General Ocean Survey and Sampling Iterative Protocol (GOSSIP). We hope that this framework will establish a consistent approach to data collection, enabling further collaboration between marine scientists from different disciplines to advance knowledge of the ocean (deep-sea and mesophotic coral ecosystems)
Machine learning in marine ecology: an overview of techniques and applications
Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio
Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model
Many interesting natural phenomena are sparsely distributed and discrete.
Locating the hotspots of such sparsely distributed phenomena is often difficult
because their density gradient is likely to be very noisy. We present a novel
approach to this search problem, where we model the co-occurrence relations
between a robot's observations with a Bayesian nonparametric topic model. This
approach makes it possible to produce a robust estimate of the spatial
distribution of the target, even in the absence of direct target observations.
We apply the proposed approach to the problem of finding the spatial locations
of the hotspots of a specific phytoplankton taxon in the ocean. We use
classified image data from Imaging FlowCytobot (IFCB), which automatically
measures individual microscopic cells and colonies of cells. Given these
individual taxon-specific observations, we learn a phytoplankton community
model that characterizes the co-occurrence relations between taxa. We present
experiments with simulated robot missions drawn from real observation data
collected during a research cruise traversing the US Atlantic coast. Our
results show that the proposed approach outperforms nearest neighbor and
k-means based methods for predicting the spatial distribution of hotspots from
in-situ observations.Comment: To appear in ICRA 2017, Singapor
A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery
With the evolution of the convolutional neural network (CNN), object detection in the
underwater environment has gained a lot of attention. However, due to the complex nature of the
underwater environment, generic CNN-based object detectors still face challenges in underwater
object detection. These challenges include image blurring, texture distortion, color shift, and scale
variation, which result in low precision and recall rates. To tackle this challenge, we propose a
detection refinement algorithm based on spatial–temporal analysis to improve the performance of
generic detectors by suppressing the false positives and recovering the missed detections in underwater
videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception,
ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus
burrows from underwater videos. Nephrops is one of the most important commercial species in
Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms.
To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz.
From experiment results, we demonstrate that the proposed framework effectively suppresses false
positives and recovers missed detections obtained from generic detectors. The mean average precision
(mAP) gained a 10% increase with the proposed refinement technique.Versión del edito
Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding
Durden J, Schoening T, Althaus F, et al. Perspectives in Visual Imaging for Marine Biology and Ecology: From Acquisition to Understanding. In: Hughes RN, Hughes DJ, Smith IP, Dale AC, eds. Oceanography and Marine Biology: An Annual Review. 54. Boca Raton: CRC Press; 2016: 1-72
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