24.8.4.2 Vehicle Detection, SAR

Chapter Contents (Back)
Vehicle Detection. SAR.
See also Vehicle Recognition, Car Recognition, Vehicle Detection.
See also Radar, SAR, Ship Detection.

Sun, G.C.[Guang-Cai], Xing, M.D.[Meng-Dao], Xia, X.G.[Xiang-Gen], Wu, Y.R.[Yi-Rong], Bao, Z.[Zheng],
Robust Ground Moving-Target Imaging Using Deramp-Keystone Processing,
GeoRS(51), No. 2, February 2013, pp. 966-982.
IEEE DOI 1302
BibRef

Huang, P.H.[Peng-Hui], Liao, G.S.[Gui-Sheng], Yang, Z.W.[Zhi-Wei], Xia, X.G.[Xiang-Gen], Ma, J.T.[Jing-Tao], Zhang, X.P.[Xue-Pan],
A Fast SAR Imaging Method for Ground Moving Target Using a Second-Order WVD Transform,
GeoRS(54), No. 4, April 2016, pp. 1940-1956.
IEEE DOI 1604
Computational complexity BibRef

Chen, L., An, D., Huang, X., Zhou, Z.,
A 3D Reconstruction Strategy of Vehicle Outline Based on Single-Pass Single-Polarization CSAR Data,
IP(26), No. 11, November 2017, pp. 5545-5554.
IEEE DOI 1709
Geometry, Image reconstruction, Imaging, Reflection, Scattering, Synthetic aperture radar, 3D reconstruction, circular synthetic aperture radar (CSAR), vehicle, outline BibRef

Huang, P.H.[Peng-Hui], Liao, G.S.[Gui-Sheng], Yang, Z.W.[Zhi-Wei], Xia, X.G.[Xiang-Gen], Ma, J.T.[Jing-Tao], Zheng, J.B.[Ji-Bin],
Ground Maneuvering Target Imaging and High-Order Motion Parameter Estimation Based on Second-Order Keystone and Generalized Hough-HAF Transform,
GeoRS(55), No. 1, January 2017, pp. 320-335.
IEEE DOI 1701
Doppler effect BibRef

Huang, P.H.[Peng-Hui], Liao, G.S.[Gui-Sheng], Yang, Z.W.[Zhi-Wei], Xia, X.G.[Xiang-Gen], Ma, J.T.[Jing-Tao], Zhang, X.P.[Xue-Pan],
An Approach for Refocusing of Ground Moving Target Without Target Motion Parameter Estimation,
GeoRS(55), No. 1, January 2017, pp. 336-350.
IEEE DOI 1701
Doppler radar BibRef

Huang, P.H.[Peng-Hui], Xia, X.G.[Xiang-Gen], Liao, G.S.[Gui-Sheng], Yang, Z.W.[Zhi-Wei], Zhou, J., Liu, X.,
Ground Moving Target Refocusing in SAR Imagery Using Scaled GHAF,
GeoRS(56), No. 2, February 2018, pp. 1030-1045.
IEEE DOI 1802
Azimuth, Doppler effect, Focusing, Frequency-domain analysis, Logic gates, Synthetic aperture radar, Transforms, synthetic aperture radar (SAR) BibRef

Huang, P.H.[Peng-Hui], Xia, X.G.[Xiang-Gen], Gao, Y., Liu, X., Liao, G.S.[Gui-Sheng], Jiang, X.,
Ground Moving Target Refocusing in SAR Imagery Based on RFRT-FrFT,
GeoRS(57), No. 8, August 2019, pp. 5476-5492.
IEEE DOI 1908
data compression, Fourier transforms, image sampling, motion compensation, radar imaging, synthetic aperture radar, synthetic aperture radar (SAR) BibRef

Li, L.[Lu], Du, Y.[Yuang], Du, L.[Lan],
Vehicle Target Detection Network in SAR Images Based on Rectangle-Invariant Rotatable Convolution,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wang, S.H.[Shi-Hong], Guo, J.Y.[Jia-Yi], Zhang, Y.T.[Yue-Ting], Wu, Y.R.[Yi-Rong],
Multi-baseline SAR 3D reconstruction of vehicle from very sparse aspects: A generative adversarial network based approach,
PandRS(197), 2023, pp. 36-55.
Elsevier DOI 2303
SAR, Very sparse aspects, Multi-baseline, Vehicle target, 3D reconstruction BibRef

Lin, X.[Xin], Zhang, B.[Bo], Wu, F.[Fan], Wang, C.[Chao], Yang, Y.[Yali], Chen, H.Q.[Hui-Qin],
SIVED: A SAR Image Dataset for Vehicle Detection Based on Rotatable Bounding Box,
RS(15), No. 11, 2023, pp. 2825.
DOI Link 2306
BibRef

Zhang, L.B.[Lin-Bin], Leng, X.G.[Xiang-Guang], Feng, S.[Sijia], Ma, X.J.[Xiao-Jie], Ji, K.[Kefeng], Kuang, G.Y.[Gang-Yao], Liu, L.[Li],
Azimuth-Aware Discriminative Representation Learning for Semi-Supervised Few-Shot SAR Vehicle Recognition,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Cui, Y.W.[Ya-Wen], Deng, W.[Wanxia], Xu, X.[Xin], Liu, Z.[Zhen], Liu, Z.[Zhong], Pietikäinen, M.[Matti], Liu, L.[Li],
Uncertainty-Guided Semi-Supervised Few-Shot Class-Incremental Learning With Knowledge Distillation,
MultMed(25), 2023, pp. 6422-6435.
IEEE DOI 2311
BibRef

Cui, Y.W.[Ya-Wen], Xiong, W.[Wuti], Tavakolian, M.[Mohammad], Liu, L.[Li],
Semi-Supervised Few-Shot Class-Incremental Learning,
ICIP21(1239-1243)
IEEE DOI 2201
Training, Image processing, Human intelligence, Benchmark testing, Image classification, Few-shot learning, incremental learning. BibRef

Cui, Y.W.[Ya-Wen], Yu, Z.T.[Zi-Tong], Peng, W.[Wei], Tian, Q.[Qi], Liu, L.[Li],
Rethinking Few-Shot Class-Incremental Learning With Open-Set Hypothesis in Hyperbolic Geometry,
MultMed(26), 2024, pp. 5897-5910.
IEEE DOI 2404
Power capacitors, Task analysis, Geometry, Measurement, Training, Protocols, Jellyfish, Few-shot learning, knowledge distillation BibRef

Song, Y.C.[Yu-Cheng], Wang, S.[Shuo], Li, Q.[Qing], Mu, H.B.[Hong-Bin], Feng, R.[Ruyi], Tian, T.[Tian], Tian, J.W.[Jin-Wen],
Vehicle Target Detection Method for Wide-Area SAR Images Based on Coarse-Grained Judgment and Fine-Grained Detection,
RS(15), No. 13, 2023, pp. 3242.
DOI Link 2307
BibRef

Liu, Z.G.[Zhi-Gang], Luo, S.J.[Sheng-Jie], Wang, Y.T.[Yi-Ting],
Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images,
RS(15), No. 18, 2023, pp. 4558.
DOI Link 2310
BibRef


Sun, Y.[Yi], Wang, W.[Wenna], Zhang, Q.Y.[Qian-Yu], Ni, H.[Han], Zhang, X.W.[Xiu-Wei],
Improved YOLOv5 with Transformer for Large Scene Military Vehicle Detection on SAR Image,
ICIVC22(87-93)
IEEE DOI 2301
Training, Costs, Object detection, Military vehicles, Transformers, Radar polarimetry, Land vehicles, large scene object detection, military vehicle BibRef

Zhang, Z.B.[Ze-Bing], Hu, W.D.[Wei-Dong],
Rectangle outline extraction of vehicles in SAR images,
IASP11(163-166).
IEEE DOI 1112
BibRef

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
ATR -- Vehicles, Aerial Images, Aircraft Detection, Recognition .


Last update:Sep 28, 2024 at 17:47:54