An enhanced spectral diversity coregistration method for dualpolarimetric Sentinel-1A/B TOPS data

Sentinel-1A/B data are crucial for retrieving numerical information about surface phenomena and processes.Coregistration of terrain observation by progressive scans(TOPS)data is a critical step in its application.TOPS data must be fundamentally co-registered with an accuracy of 0.001 pixels.However,various decorrelation factors due to natural vegetation and seasonal effects affect the coregistration accuracy of TOPS data.This paper proposed an enhanced spectral diversity coregistration method for dual-polarimetric(PolESD)Sentinel-1A/B TOPS data.The PolESD method suppresses speckle noise based on a unified non-local framework in dual-pol Synthetic Aperture Radar(SAR),and extracts the phase of the optimal polarization channel from the denoised polarimetric interferometric coherency matrix.Compared with the traditional ESD method developed for single-polarization data,the PolESD method can obtain more accurate coherence and phase and get more pixels for azimuth-offset estimation.In bare areas covered with low vegetation,the number of pixels selected by PolESD is more than the Boxcar method.It can also correct misregistration more effectively and eliminate phase jumps in the burst edge.Therefore,PolESD will help improve the application of TOPS data in low-coherence scenarios.

Robust PolInSAR optimal interferogram estimation method based on generalized scattering vector

For the polarimetric synthetic aperture radar interferometry(PolInSAR) processing, it is necessary to coregister all the images, including the coregistration of polarimetric SAR images and the coregistration of interferometric SAR images. Otherwise,the performance of the estimated optimal interferograms will be deteriorated. A generalized scattering vector(GSV) model is proposed to execute the PolInSAR optimal interferograms estimation.The generalized scattering vector is constructed by the Pauli scattering vectors of the processing pixel and the surrounding pixels.Even though there are coregistration errors, all the polarimetric information of the current processing pixel is entirely included in the generalized scattering vector. Therefore, the GSV-based method can automatically recover the optimal scattering mechanisms of the processing pixel with coregistration errors either in interferoemetric channels or polarimetric channels. Theoretical analysis and processing results of simulated Pol SARPro data and real PALSAR data validate the effectiveness and correctness of the proposed method.

arcPycor:an open-source automated GIS tool to co-register elevation datasets

Subtraction of elevation datasets(e.g.digital elevation models(DEMs)and non-continuous elevation points)acquired at different times is a useful method to monitor landform surface change.Due to heavy post-processing of these elevation datasets,multi-source errors are introduced into the resulting elevation data products.To improve the estimation of elevation change,co-registration of elevation datasets is a prerequisite.This paper presents an open-source automated GIS tool(arc Pycor)for co-registering elevation datasets.arc Pycor is coded in Python 2.7 and is run via Arc GIS for Desktop.The performances of arc Pycor have been evaluated using a series of experiments.In benchmark tests,the resolved co-registration vectors of arc Pycor are compared to the predefined shift vectors obtained by artificially misaligning the slave DEMs from the master elevation datasets.Results show that arc Pycor is able to co-register DEMs with relative high accuracy and can well align slave DEMs to non-continuous elevation points,which indicates its robustness in co-registering of elevation datasets.arc Pycor is also able to co-register multi-sourced DEMs of different resolutions in mountain areas.

SIFT-based automatic tie-points extraction for airborne InSAR images

The scale-invariant feature transform (SIFT) is often applied to extract tie-points for airborne SAR images. When a pair of airborne SAR images differs with look angles obviously, shadow sizes and shapes of same objects will differ obviously. In main and slave SAR images, key-points around shadows often match as tie-points, although they are not homologous points. The phenomenon worsens the performance of SIFT on SAR images. On the basis of SIFT, a modified matching method is proposed to decrease the number of incorrect tie-points. High-resolution airborne SAR images are used in Experiments. Experiment results show that the proposed method is very effective to extract correct tie-points for SAR images.