Ship detection oriented to compressive sensing measurements of space optical remote sensing scenes

doi:10.37188/OPE.20233104.0517

CIOMP OpenIR

	Ship detection oriented to compressive sensing measurements of space optical remote sensing scenes
	S. Xiao, Y. Zhang, X. Chang and J. Sun
	2023
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	1004924X
卷号	31 期号:4 页码:517-532
摘要	The compressive sensing（CS）-based space optical remote-sensing（SORS）imaging system can simultaneously perform sampling and compression by using hardware at the sensing stage. The system must reconstruct the original scene during the ship detection task. The scene reconstruction process of CS is computationally expensive，memory intensive，and time-consuming. This paper proposes an algorithm named compressive sensing and improved you only look once（CS-IM-YOLO）for direct ship detection based on measurements obtained by the imaging system. To simulate the block compression sampling process of the imaging system，the convolution measurement layer with the same stride and convolution kernel size is used to perform the convolution operation on the scene，and the high-dimensional image signal is projected into the low-dimensional space to obtain the full-image CS measurements. After obtaining the measurements of the scene，the proposed ship detection network extracts the coordinates of the ship from the measurements. The squeeze-and-excitation Network（SENet）module is imported into the backbone network，and the improved backbone network is used to extract the ship feature information using the measurements. The feature pyramid network is used to enhance feature extraction while fusing the feature information of the shallow，middle，and deep layers，and then to complete predicting the ship's coordinates. CS-IM-YOLO especially connects the convolutional measurement layer and the CS based ship detection network for end-to-end training；this considerably simplifies the preprocessing process. We present an evaluation of the performance of the algorithm by using the HRSC2016 dataset. The experimental results show that the precision of CS-IM-YOLO for detection of ships via CS measurements in SORS scenes is 91. 60%，the recall is 87. 59%，the F1 value is 0. 90，and the AP value is 94. 13%. This demonstrates that the algorithm can perform accurate ship detection using the CS measurements of SORS scenes. © 2023 Chinese Academy of Sciences. All rights reserved.
DOI	10.37188/OPE.20233104.0517
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收录类别	ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/68045
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	S. Xiao, Y. Zhang, X. Chang and J. Sun. Ship detection oriented to compressive sensing measurements of space optical remote sensing scenes[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2023,31(4):517-532.
APA	S. Xiao, Y. Zhang, X. Chang and J. Sun.(2023).Ship detection oriented to compressive sensing measurements of space optical remote sensing scenes.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,31(4),517-532.
MLA	S. Xiao, Y. Zhang, X. Chang and J. Sun."Ship detection oriented to compressive sensing measurements of space optical remote sensing scenes".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 31.4(2023):517-532.

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