A Beam-Segmenting Polar Format Algorithm Based on Double PCS for Video SAR Persistent Imaging

• URL: http://arxiv.org/abs/2401.10252v1
• Date: Tue, 19 Dec 2023 14:39:49 GMT
• Title: A Beam-Segmenting Polar Format Algorithm Based on Double PCS for Video SAR Persistent Imaging
• Authors: Jiawei Jiang, Yinwei Li, Shaowen Luo, Ping Li and Yiming Zhu
• Abstract summary: Video aperture radar (SAR) is attracting more attention in recent years due to its abilities of high resolution, high frame rate and advantages in continuous observation. In the process of polar format algorithm (PFA) for spotlight mode video SAR, the wavefront curvature error (WCE) limits the imaging scene size and the 2-D images affects the efficiency. To solve the aforementioned problems, a beam-segmenting PFA based on principle of chirp scaling (PCS), called BS-PCS-PFA, is proposed for video SAR imaging. The proposed method can significantly expand the effective size of PFA, and the better operational efficiency
• Score: 27.6812116360734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video synthetic aperture radar (SAR) is attracting more attention in recent years due to its abilities of high resolution, high frame rate and advantages in continuous observation. Generally, the polar format algorithm (PFA) is an efficient algorithm for spotlight mode video SAR. However, in the process of PFA, the wavefront curvature error (WCE) limits the imaging scene size and the 2-D interpolation affects the efficiency. To solve the aforementioned problems, a beam-segmenting PFA based on principle of chirp scaling (PCS), called BS-PCS-PFA, is proposed for video SAR imaging, which has the capability of persistent imaging for different carrier frequencies video SAR. Firstly, an improved PCS applicable to video SAR PFA is proposed to replace the 2-D interpolation and the coarse image in the ground output coordinate system (GOCS) is obtained. As for the distortion or defocus existing in the coarse image, a novel sub-block imaging method based on beam-segmenting fast filtering is proposed to segment the image into multiple sub-beam data, whose distortion and defocus can be ignored when the equivalent size of sub-block is smaller than the distortion negligible region. Through processing the sub-beam data and mosaicking the refocused subimages, the full image in GOCS without distortion and defocus is obtained. Moreover, a three-step MoCo method is applied to the algorithm for the adaptability to the actual irregular trajectories. The proposed method can significantly expand the effective scene size of PFA, and the better operational efficiency makes it more suitable for video SAR imaging. The feasibility of the algorithm is verified by the experimental data.

Related papers

• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
  Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications. Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors. We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
  arXiv  Detail & Related papers  (2024-09-21T04:39:45Z)
• Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
  Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. We present a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs. We also present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction.
  arXiv  Detail & Related papers  (2024-09-11T06:12:03Z)
• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution [151.1255837803585]
  We propose a novel approach, pursuing Spatial Adaptation and Temporal Coherence (SATeCo) for video super-resolution. SATeCo pivots on learning spatial-temporal guidance from low-resolution videos to calibrate both latent-space high-resolution video denoising and pixel-space video reconstruction. Experiments conducted on the REDS4 and Vid4 datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-03-25T17:59:26Z)
• Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network [26.86292926584254]
  Linear Array Pushbroom (LAP) imaging technology is widely used in the realm of remote sensing. Traditional methods for restoring LAP images, such as algorithms estimating the point spread function (PSF), exhibit limited performance. We propose a Jitter-Aware Restoration Network (JARNet) to remove the distortion and blur in two stages.
  arXiv  Detail & Related papers  (2024-01-16T07:26:26Z)
• PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks [2.580765958706854]
  Despeckling is a crucial noise reduction task in improving the quality of synthetic aperture radar (SAR) images. Existing methods deal solely with single-polarization images and cannot handle the multi-polarization images captured by modern satellites. We propose a novel self-supervised despeckling approach called channel masking, which exploits the relationship between polarizations.
  arXiv  Detail & Related papers  (2024-01-15T07:06:36Z)
• Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging [17.721259583120396]
  This paper proposes an adaptation module in a pretrained Single Image Super Resolution (SISR) network to leverage the powerful SISR model. It is an adaptation in the SISR network to further exploit the spatial and angular information in LF images to improve the super resolution performance.
  arXiv  Detail & Related papers  (2022-07-25T03:43:56Z)
• Learned Video Compression via Heterogeneous Deformable Compensation Network [78.72508633457392]
  We propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance. More specifically, the proposed algorithm extracts features from the two adjacent frames to estimate content-Neighborhood heterogeneous deformable (HetDeform) kernel offsets. Experimental results indicate that HDCVC achieves superior performance than the recent state-of-the-art learned video compression approaches.
  arXiv  Detail & Related papers  (2022-07-11T02:31:31Z)
• A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors [8.890754092562918]
  Coded aperture snapshot spectral imaging (CASSI) is a technique used to reconstruct three-dimensional hyperspectral images (HSIs) This paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors (Fama-P) of natural images.
  arXiv  Detail & Related papers  (2022-06-12T03:29:14Z)
• Optical-Flow-Reuse-Based Bidirectional Recurrent Network for Space-Time Video Super-Resolution [52.899234731501075]
  Space-time video super-resolution (ST-VSR) simultaneously increases the spatial resolution and frame rate for a given video. Existing methods typically suffer from difficulties in how to efficiently leverage information from a large range of neighboring frames. We propose a coarse-to-fine bidirectional recurrent neural network instead of using ConvLSTM to leverage knowledge between adjacent frames.
  arXiv  Detail & Related papers  (2021-10-13T15:21:30Z)
• A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet [20.217242547269947]
  A novel robust change detection approach is presented for imbalanced multi-temporal synthetic aperture radar (SAR) image based on deep learning. Our main contribution is to develop a novel method for generating difference image and a parallel fuzzy c-means (FCM) clustering method. The experimental results demonstrate that the proposed approach is effective and robust for imbalanced SAR data, and achieve up to 99.52% change detection accuracy superior to most state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-03-03T20:05:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.