###### Abstract
When the candidate regions of interest (ROIs) have been extracted by a geometric method, false alarms formed by naturally raised terrain can be eliminated by calculating the area ratio of the strong scattering region in a port. [PERSON] et al. [8] extracted a strong scattering region using a three-region level set segmentation method to eliminate the false alarms extracted by jetty scanning. In polarimetric SAR images, the scattering components of ports are complex due to the multiple scattering between buildings and the ground terrain. Thus, port detection in polarimetric SAR images can also use the specific polarimetric scattering components of port buildings and water regions. To eliminate the double-bounce scattering interference generated by a strong scatterer, [PERSON] et al. [2] extracted the volume scattering component using Freeman three-component decomposition [21] or four-component decomposition [22] to segment the sea and land. Except for the volume scattering component, polarimetric entropy [23], polarimetric cross-entropy [24], the generalized optimization of polarimetric contrast enhancement (GOPCE) [25], the similarity parameter [26], the degree of polarization [27], etc. are all useful polarimetric parameters to represent buildings on a port. However, since the scattering characteristics of buildings in a port are almost the same as those of buildings outside the port, the above parameters are difficult to directly use for port detection. Except for the geometric features of protruding jetties and breakwaters and the specific polarimetric scattering features of buildings on the port, port water is also an important and special component of a port in polarimetric SAR images. Because of the scattering interference of man-made buildings and ships in ports, the port water appears to be different from calm water regions in the Pauli pseudocolor image of polarimetric SAR. Statistics results show that the polarimetric parameters of port water, such as double-bouncescattering power and polarimetric entropy, are all different from those of calm water [2]. Thus, this paper proposes an unsupervised port detection method by extracting port water using a proposed polarimetric parameter based on three-component decomposition in polarimetric SAR images. 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. 2. A polarimetric parameter of the power ratio of the double-bounce scattering power to the volume scattering power is proposed to extract port water based on the three-component decomposition of polarimetric SAR images. 3. A simple global CFAR multi-scale thresholding segmentation method is proposed to segment port water regions and other regions. The rest of this paper is organized as follows. In Section 2, the three-component decomposition and the modified three-component decomposition are described briefly. In Section 3, the proposed method is introduced in detail. Experimental results will be shown and discussed in Section 4. The conclusion is given in Section 5.