In the past decades, Differential interferometric synthetic aperture radar (DInSAR) techniques have been employed to estimate the Earth’s surface deformation with high resolution. All pixels of monitored area are not appropriate to use by classical DInSAR techniques. The quantity of reliable pixels in the studied area and their phase quality are important factors that can limit the efficiency of DInSAR techniques. In order to avoid decorrelation influence and other limiting factors in DInSAR processing, we need to identify reliable pixels to estimate deformation rate. Permanent scatterer interferometry (PSI) technique has been extended the capability of traditional DInSAR methods as they process pixels that have partial decorrelations. These approaches employ pixels which are stable during the data acquisition. These pixels are referred as persistent scatterer (PS). In literature, PSI techniques applied on SAR data images that are usually acquired by the sensors providing single- channel data. Polarimetric capability of current satellite sensors, such as Radarsat 2, ALOS-PALSAR, TerraSAR-X, and UAVSAR, makes it possible to have SAR data with more than one channel. In this paper, we present an approach for increasing the quantity of permanent pixels. These pixels are selected for processing in DInSAR using polarimetric information prepared by new sensors. The objective of this paper is to investigate existing algorithms that confirm the contribution of polarimetric data in order to  improve persistent scatters (PS) detection. These algorithms are formulated based on two different selection criteria: amplitude dispersion index (ADI) and mean coherence. High average coherence or low ADI quantity is associated to the PS candidates and better phase quality that make deformation maps more reliable. Different approaches such as BEST, MIPO and ESM_MB are analyzed to optimize both selection criteria in terms of pixels’ quantity and density and their results are compared. Experimental results with exploiting a stack of Uninhabited Arial Vehicle Synthetic Aperture Radar (UAVSAR) quad-pol data sets over an urban area in CA, provide the expected improvement. For two criteria, the so-called BEST is the simplest method with proper computational cost time. In the MIPO approach, the polarimetric channel that maximizes the mean intensity of data over time is achieved based on eigen problem. This is one of the estimators applied over the intensity optimized data to select PSs. The ESM-MB approach provides larger improvement in both estimators. In the coherence estimator, due to computation load of the numerical solution, we tested analytical solution to find the optimum projection vector for each pixel. In ADI, due to lack of an analytical approach, we applied a numerical optimization technique for optimizing the projection vector in the ESM-MB method. Comparing the number of PSs based on quad-pol with single-pol data illustrate remarkable improvement in both selection criteria (average coherence and amplitude dispersion index). For quad-pol case, we achieve an increase of 60% with respect to single-pol data when using average coherence and over 6 times more for amplitude dispersion index. In fact, increasing the density and quality of PS, could provide deformation maps with high accuracy. The results of this paper confirm the suitability of polarimetric data in order to improve the performance of PS detection in monitored areas.