Multi-Spectral Ship Detection Using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions

Kyung-Ae Park, Jae-Jin Park, Jae-Cheol Jang, Ji-Hyun Lee, Sangwoo Oh and Moonjin Lee
Additional contact information
Kyung-Ae Park: Department of Earth Science Education/Research Institute of Oceanography, Seoul National University, Seoul 08826, Korea
Jae-Jin Park: Department of Science Education, Seoul National University, Seoul 151-748, Korea
Jae-Cheol Jang: Department of Science Education, Seoul National University, Seoul 151-748, Korea
Ji-Hyun Lee: Department of Science Education, Seoul National University, Seoul 151-748, Korea
Sangwoo Oh: Maritime Safety Research Division, Korea Research Institute of Ships and Ocean engineering, Daejeon 34103, Korea
Moonjin Lee: Maritime Safety Research Division, Korea Research Institute of Ships and Ocean engineering, Daejeon 34103, Korea

Sustainability, 2018, vol. 10, issue 11, 1-23

Abstract: The necessity of efficient monitoring of ships in coastal regions has been increasing over time. Multi-satellite observations make it possible to effectively monitor vessels. This study presents the results of ship detection methodology, applied to optical, hyperspectral, and microwave satellite images in the seas around the Korean Peninsula. Spectral matching algorithms are used to detect ships using hyperspectral images with hundreds of spectral channels and investigate the similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from the backscattering coefficients of Sentinel-1B SAR and ALOS-2 PALSAR2 images. Validation results exhibited that the locations of the satellite-detected vessels showed good agreement with real-time location data within the Sentinel-1B coverage in the Korean coastal region. This study presented the probability of detection values of optical and SAR-based ship detection and discussed potential causes of the errors. This study also suggested a possibility for real-time operational use of vessel detection from multi-satellite images based on optical, hyperspectral, and SAR remote sensing, particularly in the inaccessible coastal regions off North Korea, for comprehensive coastal management and sustainability.

Keywords: ship detection; hyperspectral; SAR; optical remote sensing; sustainability; coastal region (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/10/11/4064/pdf (application/pdf)
https://www.mdpi.com/2071-1050/10/11/4064/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:10:y:2018:i:11:p:4064-:d:180925

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().