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18 pages, 12513 KB

Open AccessArticle

Instance Segmentation of Microscopic Foraminifera

by Thomas Haugland Johansen, Steffen Aagaard Sørensen, Kajsa Møllersen and Fred Godtliebsen

Appl. Sci. 2021, 11(14), 6543; https://doi.org/10.3390/app11146543 - 16 Jul 2021

Cited by 10 | Viewed by 3392

Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of

0.78

on the classification and detection task, and

0.80

on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to

0.84

and

0.86

, respectively. Prediction results are analyzed both quantitatively and qualitatively and discussed. Based on our findings we propose several directions for future work and conclude that our proposed model is an important step towards automating the identification and counting of microscopic foraminifera. Full article

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14 pages, 2797 KB

Open AccessArticle

Compact Quantum Magnetometer System on an Agile Underwater Glider

by Brian R. Page, Reeve Lambert, Nina Mahmoudian, David H. Newby, Elizabeth L. Foley and Thomas W. Kornack

Sensors 2021, 21(4), 1092; https://doi.org/10.3390/s21041092 - 5 Feb 2021

Cited by 17 | Viewed by 8586

Abstract

This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduction process, test procedure for anomaly mapping, and results from constrained operation featuring underwater motion capture system for ground truth localization. Validation in the noisy and constrained pool environment creates a trajectory towards affordable littoral magnetic anomaly mapping infrastructure. Such a marine sensor technology will be capable of extended operation in challenging areas while providing high-resolution, timely magnetic data to operators for automated detection and classification of marine objects. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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18 pages, 3070 KB

Open AccessArticle

Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR

by Jason Sherba, Leonhard Blesius and Jerry Davis

Remote Sens. 2014, 6(5), 4043-4060; https://doi.org/10.3390/rs6054043 - 2 May 2014

Cited by 26 | Viewed by 10017

Abstract

LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer’s accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads. Full article

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