Orthorectified Polar Format Algorithm for Generalized Spotlight SAR Imaging With DEM

Abstract

In conventional polar format algorithm (PFA), the effective imaging scene is bounded to a limited region near the reference point unless postprocessing is utilized. In a previous paper, refocusing and zoom-in PFA (RZPFA) for curvilinear spotlight SAR imaging were proposed to produce a refocused image for an arbitrary region of interest (ROI) with constant elevation. However, for certain applications, the residual distortion and defocus caused by rugged terrain could not be ignored. In this article, RZPFA is adapted to incorporate the known digital elevation model (DEM) into the imaging process, which is named as orthorectified PFA (OPFA). With just little additional computations than RZPFA, OPFA can realize georeferenced orthorectified imaging via a nonuniform fast Fourier transform of type 3 (NuFFT-3) without the need of postprocessing. The quantitative metrics for the residual DEM distortion and residual DEM defocus were also derived to determine the effective imaging extent of OPFA. Within the effective extent, the proposed OPFA can obtain an orthorectified image efficiently, and the image has a very high quality comparable to backprojection (BP). The imaging results of measured echo and DEM data demonstrated the effectiveness of the proposed algorithm. Index Terms-Digital elevation model (DEM), georeferenced imaging, nonuniform fast Fourier transform, orthorectified synthetic aperture radar (SAR) imaging, polar format algorithm (PFA). I. INTRODUCTIONT HE past decades have witnessed an ever-increasing development in the fields of remote sensing [1], among which synthetic aperture radar (SAR) is of great importance for its unique all-day, all-weather sensing capability [2]. Similar to many other fields, the research of SAR has also entered the era of image processing based on deep learning [3], and commercial SAR startups are springing up [4], [5]. The research in SAR imaging has focused on algorithms for novel modes, such as bistatic SAR [6], multistatic SAR [7], multiple input multiple output SAR (MIMO-SAR

Semantic segmentation of PolSAR image data using advanced deep learning model

“…The slant to ground range conversion of the data helps in providing actual ground information to the features imaged by the SAR system. The precise terrain correction algorithms are implemented with external high-resolution DEMs for the minimization of radiometric and geometric distortions from the SAR image 58 , 59 . The use of external DEM also helps in creating a simulation-based layover-shadow mask for the orthorectified SAR data of the undulating terrain 60 , 61 .…”

A Review of Synthetic-Aperture Radar Image Formation Algorithms and Implementations: A Computational Perspective

“…There is a wide range of synthetic-aperture radar image formation algorithms. Some recent advances in improving SAR imaging algorithms can be found in [8][9][10]. The following sections describe some of the most used SAR image formation algorithms, such as: Range-Doppler, chirp scaling, omega-K, polar format, matched filter, and backprojection.…”

Squinted Airborne Synthetic Aperture Radar Imaging with Unknown Curved Trajectory

“…As a consequence, the focusing quality of the existing imaging algorithms mentioned above is strongly impaired. Principally speaking, we can get the motion information of the moving platform through orbit and altitude data provided by an ancillary instrument such as inertial measurement units (IMU), inertial navigation system (INS) and global positioning system (GPS) [ 27 , 28 , 29 , 30 , 31 ]. However, unfortunately, measurement uncertainties on the IMU, INS, GPS and so on would limit the accuracy of the moving information, and the measurement remains unknown in many real applications including some unmanned aerial vehicles without an ancillary instrument.…”