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118 results on '"Chen, Jianlai"'

2. Optical-to-SAR Translation Based on CDA-GAN for High-Quality Training Sample Generation for Ship Detection in SAR Amplitude Images.

Author

Wu, Baolong, Wang, Haonan, Zhang, Cunle, and Chen, Jianlai

Subjects
GENERATIVE adversarial networks, SYNTHETIC aperture radar, OPTICAL images, SIMILARITY (Geometry), ORBITS (Astronomy), DEEP learning
Abstract

Abundant datasets are critical to train models based on deep learning technologies for ship detection applications. Compared with optical images, ship detection based on synthetic aperture radar (SAR) (especially the high-Earth-orbit spaceborne SAR launched recently) lacks enough training samples. A novel cross-domain attention GAN (CDA-GAN) model is proposed for optical-to-SAR translation, which can generate high-quality SAR amplitude training samples of a target by optical image conversion. This high quality includes high geometry structure similarity of the target compared with the corresponding optical image and low background noise around the target. In the proposed model, the cross-domain attention mechanism and cross-domain multi-scale feature fusion are designed to improve the quality of samples for detection based on the generative adversarial network (GAN). Specifically, a cross-domain attention mechanism is designed to simultaneously emphasize discriminative features from optical images and SAR images at the same time. Moreover, a designed cross-domain multi-scale feature fusion module further emphasizes the geometric information and semantic information of the target in a feature graph from the perspective of global features. Finally, a reference loss is introduced in CDA-GAN to completely retain the extra features generated by the cross-domain attention mechanism and cross-domain multi-scale feature fusion module. Experimental results demonstrate that the training samples generated by the proposed CDA-GAN can obtain higher ship detection accuracy using real SAR data than the other state-of-the-art methods. The proposed method is generally available for different orbit SARs and can be extended to the high-Earth-orbit spaceborne SAR case. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A Review of Imaging Algorithms in Multi-platform-borne Synthetic Aperture Radar

Author

XING Mengdao, LIN Hao, CHEN Jianlai, SUN Guangcai, and YAN Bangbang

Subjects
synthetic aperture radar (sar), multi-platform-borne, radar echo model, imaging algorithms, Electricity and magnetism, QC501-766
Abstract

The multi-platform-borne Synthetic Aperture Radar (SAR) has become one of the most explored research directions in the domain of SAR. This study discusses the imaging algorithms in multi-platform-borne SARs such as airborne SAR, missile-borne SAR, and spaceborne SAR. First, the establishment of the radar echo model is briefly introduced, including two main points: slant range-model and imaging mode. Subsequently, the imaging algorithms of the aforementioned multi-platform-borne SARs developed and used in recent years are summarized. In addition, the inherent characteristics and challenges are described. Finally, the future development trends of the research are discussed.

Published
2019
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10. Nonparametric Full-Aperture Autofocus Imaging for Microwave Photonic SAR

Author

Chen, Jianlai, Xiong, Rongqi, Yu, Hanwen, Xu, Gang, and Xing, Mengdao

Abstract

The microwave photonic synthetic aperture radar (SAR) is capable of realizing large scene remote sensing observation with centimeter or even millimeter resolution, which greatly enhances the ability to acquire target information. A key issue in airborne microwave photonic SAR imaging is how to accurately correct the two-dimensional (2-D) spatial variation characteristic of the motion error. The typical two-step motion compensation (MoCo) method cannot correct the azimuth spatial variant characteristic of motion error, and the traditional subaperture methods may introduce the problems of grating lobes and image stitching. In addition, the efficiency of existing parametric full-aperture autofocus methods is usually low. To solve the above problems, a nonparametric full-aperture autofocus method based on a two-stage processing framework is proposed in this article. The first stage is to introduce a nonparametric low-order nonlinear chirp scaling (NCS) or resampling (RS) model to compensate for the low-order spatial variant motion error that accounts for the dominant component before the range cell migration correction (RCMC), which ensures that there is no significant residual RCM after the RCMC. The second stage introduces a nonparametric high-order NCS/RS model after the RCMC to compensate for the remaining high-order azimuth spatial variant phase error to achieve accurate azimuth focusing. Based on the full-aperture processing strategy, the algorithm proposed in this article avoids the problems existing in the subaperture methods. In addition, the nonparametric modeling is used throughout the autofocus processing (e.g., motion error estimation and parameter reversion of NCS/RS model), which greatly improves the efficiency of autofocus processing. The results of processing simulated and measured data verify the effectiveness of the proposed algorithm.

Published
2024
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41. Polarization Image Demosaicking via Nonlocal Sparse Tensor Factorization.

Author

Zhang, Junchao, Chen, Jianlai, Yu, Hanwen, Yang, Degui, Liang, Buge, and Xing, Mengdao

Subjects
*IMAGING systems, *FACTORIZATION, *REMOTE sensing, *POLARISCOPE, *COMPUTER vision, *IMAGE color analysis
Abstract

Division-of-focal-plane (DoFP) polarimeter provides a way for snapshot acquisition, making it available to simultaneously record polarization measurements at different orientations. This polarization imaging system has gained more attention in the last few years and is promising to be used in the fields of computer vision and remote sensing. However, this system suffers from the degradation of spatial resolution. To reconstruct polarization information at full resolution, polarization image demosaicking is indispensable. To address polarization image demosaicking issue while preserving the essential structure of polarization data, a sparse tensor factorization-based model is proposed. For a target cube, its similar cubes are first grouped together as a tensor. Then, its compact dictionary and sparse core tensor are learned by factorizing the tensor using sparse coding. Moreover, the correlation among different polarization orientations and the nonlocal self-similarity are adopted to boost the performance. Experimental results on synthetic and real-world data demonstrate that our proposed model outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality. [ABSTRACT FROM AUTHOR]

Published
2022
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44. 基于张量非负稀疏分解的偏振图像插值算法

Author

Liang Buge, Yang Degui, Chen Jianlai, Luo Haibo, and Zhang Junchao

Subjects
Physics, Factorization, Mathematical analysis, Image scaling, Tensor, Polarization (waves), Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials
Published
2021

48. Efficiency and Robustness Improvement of Airborne SAR Motion Compensation With High Resolution and Wide Swath.

Author

Chen, Jianlai, Liang, Buge, Zhang, Junchao, Yang, De-Gui, Deng, Yuhui, and Xing, Mengdao

Abstract

For airborne synthetic aperture radar (SAR) imaging with high resolution and wide swath, the atmospheric turbulence may produce serious range-dependent (RD) motion error. To estimate the RD motion error, traditional methods usually first divide the range full-aperture data into multiple range blocks, and then use phase gradient autofocus (PGA) to estimate the phase error of all range blocks one by one, which is inefficient. In addition, the robustness of PGA is also affected by the number of strong scattering points. To solve these two problems, a new motion compensation (MoCo) algorithm is proposed to improve the efficiency and robustness of airborne SAR MoCo. The real data-processing results are given to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Published
2021
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