###### 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.  
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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. The sea and land are segmented to extract the coastline using an edge-based or region-based segmentation method first [10; 11; 12; 13]. The geometric features of corners or lines are then extracted from the coastline using angular detection [14] or polygon fitting of the digital curve [15]. Sometimes, the geometric features of protruding jetties are extracted by a jetty scanning method [7]. Finally, ports are detected by measuring the parallel and broken lines [2; 4], the concavity and convexity, or the closure of the contours [6; 7; 8]. In the step of the sea-land segmentation, the existing methods usually take a two-step strategy to detect the coastline from coarse to fine. A coarse result is globally extracted by a region-based segmentation or an edge detection method first. A coarse result is then locally refined by an edge-based active contour model or Markov field segmentation. [PERSON] et al. [10] extracted coarse sea-land segmentation results using a region-based level set method and refined coarse results using an edge-based active contour model. [PERSON] et al. [11] constructed a multi-scale image space and segmented water and land from coarse to fine using the K-medians classification method in different scale-spaces. [PERSON] et al. [12] segmented water and land from coarse to fine using a narrow-band level set method in different scale-spaces. [PERSON] et al. [13] extracted rough land-sea segmentation by utilizing a texture-based segmentation using local spectral histogram and refined the coarse result using region-based level set segmentation. The existing methods are all intensity-based methods. Accurate sea-land segmentation results can be obtained when the difference of the intensity or the coherent matrix between the sea and land are distinct.