Sentienl-1 SAR 영상을 활용한 유류 분포특성과 CNN 구조에 따른 유류오염 탐지모델 성능 평가

Translated title of the contribution: Evaluation of oil spill detection models by oil spill distribution characteristics and cnn architectures using sentinel-1 sar data

Soyeon Park, Myoung Hwan Ahn, Chenglei Li, Junwoo Kim, Hyungyun Jeon, Duk Jin Kim

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures (Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

Translated title of the contributionEvaluation of oil spill detection models by oil spill distribution characteristics and cnn architectures using sentinel-1 sar data
Original languageKorean
Pages (from-to)1475-1490
Number of pages16
JournalKorean Journal of Remote Sensing
Volume37
Issue number5-13
DOIs
StatePublished - 2021

Keywords

  • Cnn
  • Oil spill detection
  • Oil spill distribution characteristics
  • Sentinel-1 sar
  • U-net

Fingerprint

Dive into the research topics of 'Evaluation of oil spill detection models by oil spill distribution characteristics and cnn architectures using sentinel-1 sar data'. Together they form a unique fingerprint.

Cite this