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Incorporating Radar Frequency-Domain Deramping into Variational Shape-Based Scene Reconstruction: A Feasibility Study Using Active Contours. [PDF]

Sensors (Basel)
Yildirim A, Bignardi S, Barnes CF, Yezzi AJ.   +3 more
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SAR Image despeckling via sparse representation

SPIE Proceedings, 2014
SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering.
Zhongmei Wang, Xiaomei Yang, Liang Zheng
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Interrupted FMCW SAR Imaging via Sparse Reconstruction

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Conventional frequency modulated continuous wave (FMCW) synthetic aperture radar(SAR) system requires two antennas. One is used to transmit pulses, the other is adopted to receive its echoes. In order to make the system low-cost and small-size, interrupted FMCW SAR is proposed.
Kang Liu, Weidong Yu
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Sparse imaging based on SAR complex image domain

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
In this paper, a novel synthetic aperture radar (SAR) imaging algorithm based on two-dimensional (2-D) compressed sensing (CS) is presented. First, a complex image is generated by applying a traditional imaging method to raw echoes. Then, the basis pursuit (BP) method is applied to a CS framework to obtain the scattering coefficients of target points ...
Wentao Lv, Junfeng Wang, Lizhong Qiu, Wenxian Yu   +3 more
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Fast sparse spectral estimation for super-resolution SAR sparse imaging

Digital Signal Processing, 2018
Abstract This paper proposes a fast algorithm for sparse spectral estimation to achieve super-resolution SAR (synthetic aperture radar) sparse imaging of complex targets. The fast algorithm used the Newton method and fast Fourier transform (FFT) based preconditioned conjugate gradient (PCG) method to estimate sparse spectral parameters efficiently ...
Yongchen Li, Ya-Qiu Jin
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SAR Image Despeckling Via Structural Sparse Representation

Sensing and Imaging, 2016
A novel synthetic aperture radar (SAR) image despeckling method based on structural sparse representation is introduced. The proposed method utilizes the fact that different regions in SAR images correspond to varying terrain reflectivity. Therefore, SAR images can be split into a heterogeneous class (with a varied terrain reflectivity) and a ...
Ting Lu, Shutao Li, Leyuan Fang, Jón Atli Benediktsson   +3 more
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SAR image despeckling with adaptive sparse representation

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2015
SR-based denoising methods have shown promising performance in image denoising. However, Because of the degradation of the noisy image, conventional SR based denoising models may not be accurate enough for the reconstruction of a clean image. Therefore, to reduce the noise corruption, a novel adaptive sparse representation based SAR image despeckling ...
Zhenchuan Pang, Guanghui Zhao, Guangming Shi, Fangfang Shen   +3 more
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Sparse and accurate high resolution SAR imaging

SPIE Proceedings, 2012
We investigate the usage of an adaptive method, the Iterative Adaptive Approach (IAA), in combination with a maximum a posteriori (MAP) estimate to reconstruct high resolution SAR images that are both sparse and accurate. IAA is a nonparametric weighted least squares algorithm that is robust and user parameter-free. IAA has been shown to reconstruct
Duc Vu, Kexin Zhao, William Rowe, Jian Li   +3 more
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SAR image edge detection via sparse representation

Soft Computing, 2017
In this paper, we propose a new synthetic aperture radar (SAR) image detection algorithm based on the de-noising algorithm via the sparse representation and a new morphology edge detector. Firstly, we apply the Shearlet transform to the SAR image to get the sparse representation of it.
Xiaole Ma, Shuaiqi Liu, Shaohai Hu, Peng Geng, Ming Liu, Jie Zhao   +5 more
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