An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

Chun Liu,Xuan Nie,Jian Yang,Jiangbin Zheng

doi:10.3390/rs14010205

Chun Liu, Xuan Nie + Show 2 more

Open Access

https://doi.org/10.3390/rs14010205

Copy DOI

Abstract

It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component decomposition and multi-scale thresholding segmentation. Firstly, the polarimetric characteristics of the port water are analyzed using modified three-component decomposition. Secondly, the volume scattering power and the power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are used to extract the port water. Water and land are first separated by a global thresholding segmentation of the volume scattering power, in which the sampling region used for the threshold calculation is automatically selected by a proposed homogeneity measure. The interference water regions in the ports are then separated from the water by segmenting the PRDV using the multi-scale thresholding segmentation method. The regions of interest (ROIs) of the ports are then extracted by determining the connected interference water regions with a large area. Finally, ports are recognized by examining the area ratio of strong scattering pixels to the land in the extracted ROIs. Seven single quad-polarization SAR images acquired by RADARSAT-2 covering the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America are used to test the proposed method. The experimental results show that all ports are correctly and quickly detected. The false alarm rates are zero, the intersection of union section (IoU) indexes between the detected port and the ground truth can reach 75%, and the average processing time can be less than 100 s.

Highlights

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Licensee MDPI, Basel, Switzerland.Ports are important stationary facilities for cargo distribution and ships to berth on the coast
The main contributions can be summarized as follows: 1. Unlike traditional methods based on the geometric features extraction of port contours or deep learning methods based on the data representation of port regions, we propose a port detection method based on port water extraction
A port detection method in polarimetric synthetic aperture radar (SAR) images has been proposed by extracting the special interference water in ports using the volume scattering power and the ratio of double-bounce to volume scattering power based on the improved three-component decomposition

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ports are important stationary facilities for cargo distribution and ships to berth on the coast. The automatic detection of ports in remote sensing images is of great significance for coastal terrains monitoring, marine navigation, terrain registration, port ship detection, port security, and port-disaster preparedness [1,2,3]. A port is located at the junction of the land and sea, which is composed of jetties, breakwaters, several man-made buildings, berthed ships, and port water. Due to the protruding jetties and breakwaters, the geometric structure of the port is so distinct that its detection is simple

Methods

Results

Conclusion