Version 1
: Received: 17 June 2024 / Approved: 17 June 2024 / Online: 17 June 2024 (12:10:45 CEST)
How to cite:
Li, Y.; Liang, J.; Yang, J.; E, X.; Zhang, Y. On the Complex-Valued Convolutional Neural Network for Oil Spill Classification based on Polarimetric Synthetic Aperture Radar. Preprints2024, 2024061152. https://doi.org/10.20944/preprints202406.1152.v1
Li, Y.; Liang, J.; Yang, J.; E, X.; Zhang, Y. On the Complex-Valued Convolutional Neural Network for Oil Spill Classification based on Polarimetric Synthetic Aperture Radar. Preprints 2024, 2024061152. https://doi.org/10.20944/preprints202406.1152.v1
Li, Y.; Liang, J.; Yang, J.; E, X.; Zhang, Y. On the Complex-Valued Convolutional Neural Network for Oil Spill Classification based on Polarimetric Synthetic Aperture Radar. Preprints2024, 2024061152. https://doi.org/10.20944/preprints202406.1152.v1
APA Style
Li, Y., Liang, J., Yang, J., E, X., & Zhang, Y. (2024). On the Complex-Valued Convolutional Neural Network for Oil Spill Classification based on Polarimetric Synthetic Aperture Radar. Preprints. https://doi.org/10.20944/preprints202406.1152.v1
Chicago/Turabian Style
Li, Y., Xinhua E and Yuanzhi Zhang. 2024 "On the Complex-Valued Convolutional Neural Network for Oil Spill Classification based on Polarimetric Synthetic Aperture Radar" Preprints. https://doi.org/10.20944/preprints202406.1152.v1
Abstract
Marine oil spills seriously threaten the coastal environment, challenge the safety of marine eco-system, and cause huge economic losses to marine fishery, aquaculture, and tourism industries. Timely and accurate oil spill monitoring and early warning can not only make the oil spill acci-dent be handled timely and effectively but also provide key information for investigating the re-sponsibility of the polluters. Polarimetric synthetic aperture radar (SAR) has shown its great po-tential in oil spill classification for its capacity to distinguish mineral oil and biogenic look-alikes. Traditional oil spill classification approaches rely on the scattering matrix elements or pre-designed scattering mechanism-based polarimetric SAR features, which limits its robustness and generalization in large-scale marine surveillance tasks. The recent development of deep learning frameworks, especially the convolutional neural network (CNN) provides a powerful tool for remote sensing image classification. However, conventional CNN only accepts re-al-valued input, which cannot efficiently analyze the polarimetric information carried by com-plex-valued SAR data. In this study, a new framework that employs the complex-valued neural network was proposed for marine oil spill detection. Sophisticated studies were designed to evaluate the contribution of the physical scattering mechanism-based features, complex-valued framework, and convolutional feature extraction structures respectively. Comparative studies were conducted on four study cases using Radarsat-2 and SIR-C fully polarimetric SAR data. The outcome of the proposed study may shed light on the development of an end-to-end marine spill classification system and enlarge its application in practical marine surveillance operations.
Keywords
Complex-valued; Convolutional Neural Network; Feature Extraction; Oil Spill; Polarimetric SAR
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
