Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network

Won-Kyung Baek,Hyung-Sup Jung

doi:10.3390/rs13163203

Abstract

It is well known that the polarization characteristics in X-band synthetic aperture radar (SAR) image analysis can provide us with additional information for marine target classification and detection. Normally, dual-and single-polarized SAR images are acquired by SAR satellites, and then we must determine how accurate the marine mapping performance from dual-polarized (pol) images is versus the marine mapping performance from the single-pol images in a given machine learning model. The purpose of this study is to compare the performance of single- and dual-pol SAR image classification achieved by the support vector machine (SVM), random forest (RF), and deep neural network (DNN) models. The test image is a TerraSAR-X dual-pol image acquired from the 2007 Kerch Strait oil spill event. For this, 824,026 pixels and 1,648,051 pixels were extracted from the image for the training and test, respectively, and sea, ship, oil, and land objects were classified from the image by using the three machine learning methods. The mean f1-scores of the SVM, RF, and DNN models resulting from the single-pol image were approximately 0.822, 0.882, and 0.889, respectively, and those from the dual-pol image were about 0.852, 0.908, and 0.898, respectively. The performance improvement achieved by dual-pol was about 3.6%, 2.9%, and 1% in SVM, RF, and DNN, respectively. The DNN model had the best performance (0.889) in the single-pol test while the RF model was best (0.908) in the dual-pol test. The performance improvement was approximately 2.1% and not noticeable. If the condition that dual-pol images have two-times lower spatial resolution versus single-pol images in the azimuth direction is considered, a small improvement may not be valuable. Therefore, the results show that the performance improvement by X-band dual-pol image may be not remarkable when classifying the sea, ships, oil spills, and sea and land surfaces.

Highlights

Synthetic aperture radar (SAR) has distinctive advantages in classifying marine features, such as ships and oil spills [1,2,3]
Intensity texture map, (iv) co-polarized interferometric coherence map, and (v) co-polarized phase difference (CPD) texture map were generated from the TerraSAR-X image
The multi-label maps, which were respectively classified from the single-pol group by the support vector machine (SVM), random forest (RF), and deep neural network (DNN)

Summary

Introduction

Synthetic aperture radar (SAR) has distinctive advantages in classifying marine features, such as ships and oil spills [1,2,3]. The radar backscattering signals from the sea, oil spills, and ships are different. The radar signal backscattered from the oil spill is reduced due to the dampened sea surface roughness [1,5,6] while the radar signal from ships is bounced more than twice between ships and the sea surface, which is called the corner effect. Oil spills have a lower brightness value, while ships have a higher brightness value compared to the surrounding sea surface on the SAR images [7,8,9,10]

Objectives

Methods

Results

Conclusion