Port Detection in Polarimetric SAR Images Based on Three-Component Decomposition

Chun, Liu; Zheng, Jiangbin; Nie, Xuan

doi:10.1109/igarss39084.2020.9324730

Cited by 2 publications

(6 citation statements)

References 6 publications

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“…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]. As shown in Figure 1, 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.…”

Section: Introductionmentioning

confidence: 99%

“…There will be many false alarms generated by the natural raised terrain using the geometric method. Because the geometric feature of ports is salient, almost all existing port and small harbor detection methods are based on the extraction of the specific geometric features [1][2][3][4][5][6][7][8][9]. Usually, the geometric-based methods mainly include three steps: sea-land segmentation, geometric feature extraction, and feature measure and classification.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…At present, related researches of harbor detection can be roughly divided into two main directions, one is based on geographic prior information [31,32], and the other is based on feature information [18,27,[33][34][35][36][37][38][39][40][41][42]. These two types of methods both have effective detection performance in some cases, but there are some limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Feature-based detection methods usually implement harbor detection by extracting key features, which can also be roughly divided into two types. One is based on coastline closure [33][34][35][36][37], and the other is based on wharf features [18,27,[38][39][40][41][42]. The methods based on coastline closure [33][34][35][36][37] are generally designed according to two characteristics, one characteristic is the strong closure of the harbor coastline, and the other is that the contour of the harbor coastline is far longer than the distance of the harbor gate.…”

Section: Introductionmentioning

confidence: 99%

“…They put forward the idea of detecting transition points to get the positions of changes, which also provides a valuable approach for detecting specific regions in SAR images. Therefore, the methods based on wharf features usually detect the harbors by extracting key points and geometric features of harbors' contours [18,27,[38][39][40][41][42]. The method has a remarkable effect when the wharf has obvious outline characteristics and good straightness characteristics, however, due to the influence of coherent speckle noise and docked ships, these features are often fuzzy in SAR images.…”

Section: Introductionmentioning

confidence: 99%

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A Multidirectional One-Dimensional Scanning Method for Harbor Detection From SAR Images

Wang¹,

Pei²,

Xu³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the development of economic globalization, coastal harbors have become an increasingly important gateway for international trade. Synthetic aperture radar (SAR) is an important microwave sensor with high resolution imaging capability. Harbor detection in SAR images is of great significance for timely obtaining coastal intelligence of a country. However, due to its complexity and diversity, harbor detection is challenging. In this paper, a harbor detection method based on multidirectional one-dimensional scanning is proposed. First, discrete coastline to obtain control points, and multiple directions are selected at each control point, values of scanned pixels in each direction are recorded to obtain the representation vector. Then, design a onedimensional convolutional neural network for harbor features identification of representation vectors. Finally, the harbors are located by clustering feature points. The proposed method can extract vectors to characterize the distribution of land, waters, and wharfs. Therefore, the problem of object detection in twodimensional images is transformed into the identification of representation vectors. The method is effective for harbors with various scales and forms and has low computational complexity. Experimental results on spaceborne SAR images demonstrate the effectiveness of the proposed method.

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Port Detection in Polarimetric SAR Images Based on Three-Component Decomposition

Cited by 2 publications

References 6 publications

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

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

A Multidirectional One-Dimensional Scanning Method for Harbor Detection From SAR Images

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