基于频谱残留变换的红外遥感图像舰船目标检测方法

张志龙; 杨卫平; 张焱; 李吉成

doi:10.11999/JEIT141659

基于频谱残留变换的红外遥感图像舰船目标检测方法

doi: 10.11999/JEIT141659

基金项目:

国家自然科学基金(61101185, 61302145)和国家专项课题(0404040604)资助课题

计量
- 文章访问数: 1555
- HTML全文浏览量: 111
- PDF下载量: 671
- 被引次数: 0
出版历程
- 收稿日期: 2014-12-16
- 修回日期: 2015-05-18
- 刊出日期: 2015-09-19

Ship Detection in Infrared Remote Sensing Images Based on Spectral Residual Transform

摘要

摘要: 该文提出一种基于频谱残留变换的红外遥感图像舰船目标检测方法。该方法首先根据海洋红外图像中自然背景和干扰的特性设计频谱残留变换的模型参数；然后对海洋红外图像实施频谱残留变换；最后在变换图像上进行目标检测。实验结果表明：该方法可以有效消除红外图像中的大尺度干扰和图像噪声，增强图像中舰船目标的信杂比，提高舰船检测的准确性。
- 红外遥感图像 /
- 舰船目标检测 /
- 信杂比 /
- 视觉显著性 /
- 谱残留模型
Abstract: A ship detection algorithm based on spectral residual transform is presented to detect ship in infrared remote sensing images. Firstly, the model parameters of spectral residual transform are designed according to the prior knowledge of ship and its natural backgrounds. Secondly, the spectral residual transform of sea infrared image is implemented. Thirdly, ship detection is done on the spectral residual transform image. Experimental results reveal that the new detection algorithm can remove large scale image interference and the image noise and improve the SCR of ship image. The detecting probability of the new algorithm is higher than other conventional methods.
- Infrared remote sensing images /
- Ship target detection /
- Signal Clatter Ratio (SCR) /
- Visual saliency /
- Spectral residual model

HTML全文

参考文献(21)

Cheng Gong, Han Jun-wei, Guo Lei, et al.. Object detection in remote sensing imagery using a discriminatively trained mixture model[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, (85): 32-43.

Cheng Gong, Han Jun-wei, Zhou Pei-cheng, et al.. Multi- class geospatial object detection and geographic image classification based on collection of part detectors[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, (98): 119-132.

Han Jun-wei, Zhou Pei-cheng, Zhang Ding-wen, et al.. Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, (89): 37-48.

An Zhen-yu, Shi Zhen-wei, Teng Xichao, et al.. An automated airplane detection system for large panchromaticimage with high spatial resolution[J]. Optik- International Journal for Light and Electron Optics, 2014, 125(12): 2768-2775.

Zhang Wan-ceng, Sun Xian, Wang Hong-qi, et al.. A generic discriminative part-based model for geospatial object detection in optical remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 99(1): 30-44.

张亮. SAR 图像舰船目标检测方法研究[D]. [硕士论文], 国防科学技术大学, 2007.

Zhang Liang. Detection of ship in SAR image[D]. [Master dissertation], National University of Defense Technology, 2007.

陈海亮. 基于特征的光学遥感图像舰船目标检测技术研究[D]. [硕士论文], 国防科学技术大学, 2010.

Chen Hai-liang. Feature based method for ship detection in optical remote sensing image[D]. [Master dissertation], Graduate School of National University of Defense Technology, 2010.

Hou X and Zhang L. Saliency detection: a spectral residual approach[C]. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, Minnesota, USA, 2007: 1-8.

Harel J, Koch C, and Perona P. Graph-based visual saliency[C]. Proceedings of the 20th Neural Information Processing Systems (NIPS 2006), Vancouver, British Columbia, Canada, 2006: 545-552.

Achanta R, Estrada F, Wils P, et al.. Salient region detection and segmentation[C]. Proceedings of the 6th International Conference on Computer Vision Systems (ICVS 2008), Santorini, Greece, 2008: 66-75.

Achanta R, Hemami S, Estrada F, et al.. Frequency-tuned salient region detection[C]. Proceedings of the 2009 Computer Vision and Pattern Recognition, 2009(CVPR 2009), Miami, Florida, USA, 2009: 1597-1604.

Itti L, Koch C, and Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.

许军毅, 计科峰, 雷琳, 等. 基于GLRT的光学卫星遥感图像舰船目标检测[J]. 遥感技术与应用, 2012, 27(4): 616-622.

Xu Jun-yi, Ji Ke-feng, Lei Lin, et al.. Ship target detection from optical satellite remote sensing image based on GLRT [J]. Remote Sensing Technology and Application, 2012, 27(4): 616-622.

Smith M E and Varshney P K. Intelligent CFAR processor based on data variability[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 837-847.

Farrouki A and Barkat M. Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments[J]. IEE Proceedings-Radar, Sonar and Navigation, 2005, 152(1): 43-51.

Harm G. Developments in detection algorithms at JRC[C]. The Third Meeting of the DECLIMS Project, Vancouver, BC, 2004: 1-7.

唐沐恩, 林挺强, 文贡坚. 遥感图像中舰船检测方法综述[J].计算机应用研究, 2011, 28(1): 29-36.

Tang Mu-en, Lin Ting-qiang, and Wen Gong-jian. Overview of ship detection methods in remote sensing image[J]. Application Research of Computers, 2011, 28(1): 29-36.

施引文献

资源附件(0)

访问统计