[1] [1] 1曹佃生， 石振华， 林冠宇. 机载海洋改进型Dyson高光谱成像仪的研制［J］. 光学 精密工程， 2017， 25（6）： 1403-1409. doi: 10.3788/OPE.20172506.1403CAOD SH， SHIZH H， LING Y. Development of airborne ocean modified Dyson hyperspectral imager［J］. Opt. Precision Eng.， 2017， 25（6）： 1403-1409.（in Chinese）. doi: 10.3788/OPE.20172506.1403

[2] [2] 2赵云峰， 李夜金， 张寅， 等. 海洋背景下运动目标的天基红外探测场景生成系统［J］. 光学 精密工程， 2017， 25（4）： 487-493. doi: 10.3788/ope.20172504.1019ZHAOY F， LIY J， ZHANGY， et al. A space-based infrared detection scene generation system for moving objects with sea background［J］. Opt. Precision Eng.， 2017， 25（4）： 487-493.（in Chinese）. doi: 10.3788/ope.20172504.1019

[3] [3] 3姜鑫， 陈武雄， 聂海涛， 等. 航空遥感影像的实时舰船目标检测［J］. 光学 精密工程， 2020， 28（10）： 2360-2369. doi: 10.37188/ope.20202810.2360JIANGX， CHENW X， NIEH T， et al. Real-time ship target detection based on aerial remote sensing images［J］. Opt. Precision Eng.， 2020， 28（10）： 2360-2369.（in Chinese）. doi: 10.37188/ope.20202810.2360

[4] [4] 4王慧利， 朱明， 蔺春波， 等. 光学遥感图像中复杂海背景下的舰船检测［J］. 光学 精密工程， 2018， 26（3）： 723-732. doi: 10.3788/ope.20182603.0723WANGH L， ZHUM， LINCH B， et al. Ship detection of complex sea background in optical remote sensing images［J］. Opt. Precision Eng.， 2018， 26（3）： 723-732.（in Chinese）. doi: 10.3788/ope.20182603.0723

[5] JAYAWEERA S K[M]. Signal Processing for Cognitive Radios(2015).

[6] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).

[7] CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles： exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 52, 489-509(2006).

[8] RICHARD BARANIUK. A lecture on compressive sensing[J]. IEEE Signal Processing Magazine, 24, 1-9(2007).

[9] [9] 9刘吉英，朱炬波，严奉霞，等. 基于压缩感知理论的稀疏遥感成像系统设计［J］. 系统工程与电子技术，2010，32（8）：1618-1623. doi: 10.3969/j.issn.1001-506X.2010.08.14LIUJ J， ZHUJ B， YANF X， et al. Design of remote sensing imaging system based on compressive sensing［J］. Systems Engineering and Electronics， 2010，32（8）：1618-1623.（in Chinese）. doi: 10.3969/j.issn.1001-506X.2010.08.14

[10] ZHANG X D, XIE J N, LI C L et al. MEMS-based super-resolution remote sensing system using compressive sensing[J]. Optics Communications, 426, 410-417(2018).

[11] LIU J, ZHU J, YAN F et al. Theoretical frameworks of remote sensing systems based on compressive sensing[J]. Mathematics(2010).

[12] DU J, XIE X M, WANG C Y et al. Color image reconstruction with perceptual compressive sensing[C], 1512-1517(2018).

[13] LOHIT S, KULKARNI K, KERVICHE R et al. Convolutional neural networks for noniterative reconstruction of compressively sensed images[J]. IEEE Transactions on Computational Imaging, 4, 326-340(2018).

[14] YAO H T, DAI F, ZHANG S L et al. DR²-Net： deep Residual Reconstruction Network for image compressive sensing[J]. Neurocomputing, 359, 483-493(2019).

[15] GUO H Y, YANG X, WANG N N et al. A rotational libra R-CNN method for ship detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 5772-5781(2020).

[16] WANG Z Q, ZHOU Y, WANG F T et al. SDGH-net： ship detection in optical remote sensing images based on Gaussian heatmap regression[J]. Remote Sensing, 13, 499(2021).

[17] WANG Q, SHEN F Y, CHENG L F et al. Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images[J]. International Journal of Remote Sensing, 42, 520-536(2021).

[18] WANG Y Y, WANG C, ZHANG H et al. Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery[J]. Remote Sensing, 11(2019).

[19] [19] 19魏玮， 杨茹， 朱叶. 改进CenterNet的遥感图像目标检测［J］. 计算机工程与应用， 2021， 57（6）： 191-199.WEIW， YANGR， ZHUY. Target detection of improved CenterNet to remote sensing images［J］. Computer Engineering and Applications， 2021， 57（6）： 191-199.（in Chinese）

[20] [20] 20胡炎， 单子力， 高峰. 基于Faster-RCNN和多分辨率SAR的海上舰船目标检测［J］. 无线电工程， 2018， 48（2）： 96-100. doi: 10.3969/j.issn.1003-3106.2018.02.04HUY， SHANZ L， GAOF. Ship detection based on faster-RCNN and multiresolution SAR［J］. Radio Engineering， 2018， 48（2）： 96-100.（in Chinese）. doi: 10.3969/j.issn.1003-3106.2018.02.04

[21] [21] 21陈科峻， 张叶. 基于YOLO-v3模型压缩的卫星图像船只实时检测［J］. 液晶与显示， 2020， 35（11）： 1168-1176. doi: 10.37188/yjyxs20203511.1168CHENK J， ZHANGY. Real-time ship detection in satellite images based on YOLO-v3model compression［J］. Chinese Journal of Liquid Crystals and Displays， 2020， 35（11）： 1168-1176.（in Chinese）. doi: 10.37188/yjyxs20203511.1168

[22] FU D, GUIMARAES G. Using compression to speed up image classification in artificial neural networks[webpage]. https：//www.danfu.org/files/CompressionImageClassification.pdf

[23] TORFASON R, MENTZER F, AGUSTSSON E et al. Towards image understanding from deep compression without decoding[webpage]. arXiv, 1803-06131(2018). https：//arxiv.org/abs/1803.06131

[24] DEGUERRE B, CHATELAIN C, GASSO G. Fast object detection in compressed JPEG Images[C], 333-338(2019).

[25] SHI W Z, JIANG F, LIU S H et al. Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 29, 375-388(2020).

[26] ZHAO Z, XIE X, WANG C et al. ROI-CSNet： Compressive sensing network for ROI-aware image recovery[J]. Signal Processing： Image Communication, 78, 113-124(2019).

[27] KULKARNI K, LOHIT S, TURAGA P et al. ReconNet： non-iterative reconstruction of images from compressively sensed measurements[C], 449-458(2016).

[28] YAO H T, DAI F, ZHANG S L et al. DR²-Net： deep Residual Reconstruction Network for image compressive sensing[J]. Neurocomputing, 359, 483-493(2019).

[29] REDMON J, FARHADI A. YOLOv3： an incremental improvement[webpage]. arXiv, 1804-02767(2018). https：//arxiv.org/abs/1804.02767

[30] LIN T Y, GOYAL P, GIRSHICK R et al. Focal Loss for Dense Object Detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 99, 2999-3007(2017).

[31] DUAN K, BAI S, XIE L et al. CenterNet： Object Detection with Keypoint Triplets[webpage]. https：//arxiv.org/pdf/1904.08189v1.pdf

[32] WANG Q, SHEN F Y, CHENG L F et al. Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images[J]. International Journal of Remote Sensing, 42, 520-536(2021).

[33] LIU Z K, WANG H Z, WENG L B et al. Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds[J]. IEEE Geoscience and Remote Sensing Letters, 13, 1074-1078(2016).