Persistent Scatterer Interferometry (PSI) is a technique to detect and analysis of a network of coherent pixels referred as Permanent/Persistent scatterer (PS) which are stable throughout time-series of SAR images. This technique has been applied to monitor and measure phenomena such as earth subsidence fault movements and earthquake volcanic activity and other geological and environmental studies. In all PSI techniques, the processing is carried out only on the PS pixels. Therefore, high density and phase quality of these pixels are the most effective factors on the results of these techniques. The main challenge of this technique is to detect the coherent pixels over non-urban areas which suffer from the temporal decorrelation.
Nowdays and with the development of polarimetric SAR sensors, polarimetric radar data are available. Polarimetric data consist of several conventional SAR acquisitions, usually addressed as channels. Each channel in a PolSAR acquisition is sensitive to different geometric characteristics of the scene. This additional redundancy over the scene may allow to increase both quality and density of the PS pixels. Therefore, the combination of polarimetry and interferometry enables to improve the effectiveness of PSI techniques, especially in non-urban areas.
In this paper, we employ a method to improve the conventional PSInSAR algorithm for detecting PSC by using polarimetric optimization method on dual-pol SAR data. The improvement of this research is based on minimizing ADI criterion by means of an Exhaustive Search Polarimetric Optimization method to increase the number of PSCs.

Materials & Methods

2.1 Dataset Description

The proposed method is tested using a dataset of 17 dual-pol SAR data (VV/VH) acquired by Sentinel1-A satellite March 2017 and October 2017.

2.2 Polarimetric SAR Interferometry

A general formulation for polarimetric SAR interferometry was proposed in (Cloude & Papathanassiou, 1997). The scattering matrix S represents the polarimetric information associated to each pixel of the scene. Considering a monostatic configuration, the scattering matrix S is defined as follows:

(1)

where , are copolar terms, is the cross polar term. This can be represented with the target scattering vector using the Pauli basis as:

(2)

where is the transpose operator. In Sentinel-1configuration (VV/VH), where there is no knowledge of , scattering vector can be written as:

(3)

In order to generate an interferogram, each target vector can be projected onto a unitary complex vector . Result of this step obtains the scattering coefficient μ defined as:

(4)

where i corresponds to two images, and * represents the conjugate operator. The scattering coefficient μ is a new channel or polarization state which is a linear combination of the Pauli vector elements. In this regard, all interferometry techniques can be extended from single-pol configuration to a desired polarization state by using (4) and (5). The projection vector for dual-pol data defined as:

(5)

where and are two real parameters whose ranges are finite and known and are related to the geometrical and electromagnetic properties of the targets. The parameter represents the type of scattering mechanism and corresponds to orientation of scattering. In our research, the main purpose of polarimetric optimization is to search in a two-dimensional space, and , to find an optimum projection vector, .

2.3 Amplitude Dispersion Index Optimization

In order to generate a polarimetric form of , it is sufficient to replace scattering coefficient, , in (6) by as define in (4):

(6)

(7)

The main issue in the ADI optimization is finding a projection vector for each pixel, which leads to minimize the value.

Results & Discussion

Our results confirm that the algorithm substantially improves the PSInSAR performance, increasing the number of PS pixels with respect to standard PSI, and increasing the phase quality of selected pixels. The results reveal that using the optimum scattering mechanism increases the number of PSC about 2.6 times and PS density about 2 times than using single channel datasets. Also, the effectiveness of the method is evaluated in urban and non-urban regions. The experimental results showed that the method was successful to increase the final set of PS pixels in both regions significantly.

Conclusion

In summary, it can be inferred that the polarimetric optimization method is successful to increase the number of the final set of PS pixels in different regions, significantly.