[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] S K JAYAWEERA. Signal Processing for Cognitive Radios(2015).
[6] D L DONOHO. Compressed sensing. IEEE Transactions on Information Theory, 52, 1289-1306(2006).
[7] E J CANDES, J ROMBERG, T TAO. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52, 489-509(2006).
[8] BARANIUK RICHARD. A lecture on compressive sensing. IEEE Signal Processing Magazine, 24, 1-9(2007).
[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] X D ZHANG, J N XIE, C L LI et al. MEMS-based super-resolution remote sensing system using compressive sensing. Optics Communications, 426, 410-417(2018).
[11] J LIU, J ZHU, F YAN et al. Theoretical frameworks of remote sensing systems based on compressive sensing. Mathematics(2010).
[12] J DU, X M XIE, C Y WANG et al. Color image reconstruction with perceptual compressive sensing, 1512-1517(2018).
[13] S LOHIT, K KULKARNI, R KERVICHE et al. Convolutional neural networks for noniterative reconstruction of compressively sensed images. IEEE Transactions on Computational Imaging, 4, 326-340(2018).
[14] H T YAO, F DAI, S L ZHANG et al. DR2-Net: deep Residual Reconstruction Network for image compressive sensing. Neurocomputing, 359, 483-493(2019).
[15] H Y GUO, X YANG, N N WANG et al. A rotational libra R-CNN method for ship detection. IEEE Transactions on Geoscience and Remote Sensing, 58, 5772-5781(2020).
[16] Z Q WANG, Y ZHOU, F T WANG et al. SDGH-net: ship detection in optical remote sensing images based on Gaussian heatmap regression. Remote Sensing, 13, 499(2021).
[17] Q WANG, F Y SHEN, L F CHENG et al. Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images. International Journal of Remote Sensing, 42, 520-536(2021).
[18] Y Y WANG, C WANG, H ZHANG et al. Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery. Remote Sensing, 11(2019).
[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
[24] B DEGUERRE, C CHATELAIN, G GASSO. Fast object detection in compressed JPEG Images, 333-338(2019).
[25] W Z SHI, F JIANG, S H LIU et al. Image compressed sensing using convolutional neural network. IEEE Transactions on Image Processing, 29, 375-388(2020).
[26] Z ZHAO, X XIE, C WANG et al. ROI-CSNet: Compressive sensing network for ROI-aware image recovery. Signal Processing: Image Communication, 78, 113-124(2019).
[27] K KULKARNI, S LOHIT, P TURAGA et al. ReconNet: non-iterative reconstruction of images from compressively sensed measurements, 449-458(2016).
[28] H T YAO, F DAI, S L ZHANG et al. DR2-Net: deep Residual Reconstruction Network for image compressive sensing. Neurocomputing, 359, 483-493(2019).
[30] T Y LIN, P GOYAL, R GIRSHICK et al. Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis & Machine Intelligence, 99, 2999-3007(2017).
[32] Q WANG, F Y SHEN, L F CHENG et al. Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images. International Journal of Remote Sensing, 42, 520-536(2021).
[33] Z K LIU, H Z WANG, L B WENG et al. Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds. IEEE Geoscience and Remote Sensing Letters, 13, 1074-1078(2016).