Open Access
Open access
Remote Sensing, volume 13, issue 13, pages 2536

Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection

Sara Freitas 1
Hugo Silva 1
Eduardo Silva 1, 2
1
 
INESCTEC—Institute for Systems and Computer Engineering, Technology and Science, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Publication typeJournal Article
Publication date2021-06-29
Journal: Remote Sensing
scimago Q1
SJR1.091
CiteScore8.3
Impact factor4.2
ISSN20724292, 23154632, 23154675
General Earth and Planetary Sciences
Abstract

This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

Found 
Found 

Top-30

Journals

1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
9

Publishers

2
4
6
8
10
12
14
2
4
6
8
10
12
14
  • We do not take into account publications without a DOI.
  • Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Share
Cite this
GOST | RIS | BibTex | MLA
Found error?