Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm

HUANG Yong; LIU Fang

doi:10.11999/JEIT160650

Volume 39 Issue 4

Apr. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(4): 968-972

HUANG Yong, LIU Fang. Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650

Citation:

HUANG Yong, LIU Fang. Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650

HUANG Yong, LIU Fang. Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650

Citation:

HUANG Yong, LIU Fang. Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650

PDF( 3428 KB)

Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm

doi: 10.11999/JEIT160650

HUANG Yong¹,
LIU Fang¹

Funds:

The National 973 Program of China (2013CB329402), The National Natural Science Foundation of China (61573267), The Major Research Plan of the National Natural Science Foundation of China (91438201, 91438103)

Received Date: 2016-06-21
Rev Recd Date: 2016-11-07
Publish Date: 2017-04-19

Abstract

Abstract

It is hard to select a probability distribution model for very high resolution SAR images. This paper presents a novel method for the automatic detecting of cars from VHR SAR image without the probability distribution model. The proposed method starts with searching bright regions and dark regions by the gray feature. Subsequently, the fuzzy membership is employed to extract the semantic features of car from bright regions and dark regions. The potential scattering surface and shadow are matched and calculated with the spatial semantic relationship. Finally, the cars are selected from the matching. The efficiency of the proposed method is demonstrated by experiment which shows it still has high detection rate without the probability distribution model.
- Very High Resolution SAR (VHR SAR),
- Image processing,
- Object detection,
- Image semantic,
- Fuzzy membership

FullText(HTML)

References(22)

References

EL-DARYMLI K, MCGUIRE P, POWER D, et al. Target detection in synthetic aperture radar imagery: A state-of-the- art survey[J]. Journal of Applied Remote Sensing, 2013, 7(1): 1-35. doi: 10.1117/1.JRS.7.071598.

ZHANG Yangrui, GAO Meiguo, and LI Yunjie. Performance analysis of typical mean-level CFAR detectors in the interfering target background[C]. IEEE 9th Conference on (Industrial Electronics and Applications) ICIEA, Hangzhou, 2014: 1045-1048.

YU Wenyi, WANG Yinghua, LIU Hongwei, et al. Superpixel- based CFAR target detection for high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 730-734. doi: 10.1109/LGRS.2016.2540809.

HUANG Yong and LIU Fang. Detecting cars in VHR SAR images via semantic CFAR algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6): 801-805. doi: 10. 1109/LGRS.2016.2546309.

HOU Biao, CHENG Xingzhong, and JIAO Licheng. Multilayer CFAR detection of ship targets in very high resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 811-815.

宋文青, 王英华, 刘宏伟. 高分辨SAR图像自动区域筛选目标检测算法[J]. 电子与信息学报, 2016, 38(5): 1017-1025. doi: 10.11999/JEIT150808.

SONG Wenqing, WANG Yinghua, and LIU Hongwei. An automatic block-to-block censoring target detector for high resolution SAR image[J]. Journal of Electronics Information Technology, 2016, 38(5): 1017-1025. doi: 10.11999/JEIT150808.

DOERRY A W and DUBBERT D F. Digital signal processing applications in high-performance synthetic aperture radar processing[C]. Signals, Systems and Computers, Monterey, USA, 2004: 947-949.

BRENER A R. Proof of concept for airborne SAR imaging with 5 cm resolution in the X-band[C]. European Conference on Synthetic Aperture Radar, Aachen, Germany, 2010: 615-618.

王岩飞, 刘畅, 李和平, 等. 基于多通道合成的优于0.1 m分辨率的机载SAR系统[J]. 电子与信息学报, 2013, 35(1): 29-35. doi: 10.3724/SP. J.1146.2011.01370.

WANG Yanfei, LIU Chang, and LI Heping, et al. An airborne SAR with 0.1 m resolution using multi-channel synthetic bandwidth[J]. Journal of Electronics Information Technology, 2013, 35(1): 29-35. doi: 10.3724/SP.J.1146.2011. 01370.

CHABBI S, LAROUSSI T, and BARKAT M. Performance analysis of dual automatic censoring and detection in heterogeneous Weibull clutter: A comparison through extensive simulations[J]. Signal Processing, 2013, 99(11): 2879-2893. doi: j.sigpro.2013.03.026.

GAO Gui, LIU Li, and ZHAO Lingjun, et al. An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1685-1697. doi: 10.1109/TGRS. 2008.2006504.

ANANTHI V P, BALASUBRAMANIAM P, and LIM C P. Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions[J]. Pattern Recognition, 2014, 47(12): 3870-3880.

KRINIDIS S and CHATZIS V. A robust fuzzy local information C-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1336.

WANG Min and SONG Tengyi. Remote sensing image retrieval by scene semantic matching[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 2874-2886.

FERRO A, BRUNNER D, and BRUZZONE L. Automatic detection and reconstruction of building radar footprints from single VHR SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 935-952.

JIAO Licheng, TANG X, HOU B, et al. SAR images retrieval based on semantic classification and region-based similarity measure for earth observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3876-3891.

WANG H F and SUN Z X. The methods of semantics processing in content-based image retrieval[J]. Journal of Image Graph, 2001, 6(10): 945-952.

LIU Fang, DUAN Yiping, LI Lingling, et al. SAR image segmenation based on hierarhical visual semantic and adaptive neighborhood multinomial latent model[J]. IEEE Trancactions on Geoscience and Remote Sensing, 2016, 54(7): 4287-4301.

WU N and SONG F M. An image retrieval method based on high-level image semantic information[J]. Journal of Image Graph, 2006, 11(12): 1774-1780.

LIAO Pingsung, CHEN Tsesheng, and CHUNG Pauchoo. A fast algorithm for multilevel thresholding[J]. Journal of Information Science and Engineering, 2001, 17(5): 713-727.

Relative Articles

Supplements(0)

Cited By

Proportional views