面向航天光学遥感场景压缩感知测量值的舰船检测

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

基于压缩感知的航天光学遥感成像系统可以在采样阶段通过硬件同时完成采样和压缩。在面临舰船检测任务时,系统需要重建原始场景,CS的场景重建过程计算量大、内存要求高且耗时。本文提出了直接对成像系统测量值进行舰船检测的算法——基于压缩感知和改进YOLO的测量值舰船检测算法。为了模拟成像系统的分块压缩采样过程,利用步长和卷积核尺寸相等的卷积测量层对场景进行卷积运算,将高维图像信号投影到低维空间得到全图CS测量值。得到场景的测量值后,测量值舰船检测网络从测量值中提取舰船的位置信息。在主干网络中导入SENet模块,利用改进后的主干网络来提取测量值的舰船特征信息;利用特征金字塔网络强化特征提取的同时融合浅层、中层和高层的特征信息,进而完成舰船的位置信息预测。其中,CS-IM-YOLO将卷积测量层和CS测量值舰船检测网络连接起来端对端训练,大大简化了算法的预处理过程。通过数据集HRSC2016评测算法性能,实验结果表明:CS-IM-YOLO对于SORS场景CS测量值舰船检测的检测精度为91.60%,召回率为87.59%,F1值为0.90,和AP值为94.13%。这充分表明该算法可以对SORS场景的CS测量值进行高质量的舰船检测。

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.