[1] HU L, ZHANG J, GAO F. A building extraction method using shadow in high resolution multispectral images[C]∥Proceedings of International Geoscience and Remote Sensing Symposium. Vancouver, BC, Canada: IEEE, 2011: 24-29. [2] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, US:IEEE, 2014: 580-587. [3] GIRSHICK R. Fast R-CNN[C]∥Proceedings of IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1440-1448. [4] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]∥Proceedings of the 2015 neural information processing systems(NIPS). New York,NY,US: Curran Associates Inc., 2015: 91- 99. [5] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, US:IEEE, 2016: 779-788. [6] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]∥Proceedingts of IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, US: IEEE, 2017: 6517-6525. [7] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL].[2021-04-05].http:∥arxiv.org/abs/1804.02767. [8] BOCHKOVSKIY A, WANG C Y, LIAO H. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. [2021-04-05].http:∥arxiv.org/abs/2004.10934. [9] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]∥Proceedings of European Conference on Computer Vision. Berlin,Germany: Springer, 2016: 21-37. [10] ZHOU X Y, WANG D Q, KRHENB?HL P. Objects as points[EB/OL].[2019-04-26].http:∥arxiv.org/abs/1904.07850. [11] SHAHZAD M, MAURER M, FRAUNDORFER F, et al. Buildings detection in VHR SAR images using fully convolution neural networks [J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 1100-1116. [12] ZHU X X, BAMLER R. Very high resolution space borne SAR tomography in urban environment [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4296-4308. [13] LI J, ZHANG R, LI Y. Multiscale convolutional neural network for the detection of built-up areas in high-resolution SAR images[C]∥Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Piscataway,NJ,US: IEEE, 2016: 910-913. [14] WU Y, ZHANG R, LI Y. The detection of built-up areas in high-resolution SAR images based on deep neural networks[C]∥Proceedings of International Conference on Image and Graphics. Berlin,Germany:Springer, 2017: 646-655. [15] AN Q, PAN Z, LIU L, et al. DRBox-v2: an improved detector with rotatable boxes for target detection in SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(99): 8333-8349. [16] YANG X, YANG J, YAN J, et al. SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects[C]∥Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway,NJ,US: IEEE, 2019: 8231-8240. [17] YANG X, LIU Q, YAN J, et al. R3Det: refined single-stage detector with feature refinement for rotating object[EB/OL].[2021-04-05].http:∥arxiv.org/abs/1908.05612. [18] ZHOU X Y, YAO C, WEN H, et al. EAST: an efficient and accurate scene text detector[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI,US:IEEE, 2017: 2642-2651. [19] LIU X B, LIANG D, YAN S, et al. FOTS: fast oriented text spotting with a unified network[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT,US:IEEE, 2018: 5676-5685. [20] HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL].[2017-05-27].http:∥arxiv.org/abs/1704.04861. [21] SANDLER M, HOWARD A G, ZHU M L, et al. Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation[EB/OL]. [2018-01-12].http:∥arxiv.org/abs/1801.04381. [22] HOWARD A, SANDLER M, CHU G, et al. Searching for MobileNetV3[C]∥Proceedings of IEEE International Conference on Computer Vision. Seoul, South Korea:IEEE, 2019: 1314-1324. [23] HAN K, WANG Y H, TIAN Q, et al. GhostNet: more features from cheap operations[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Seattle, WA,US:IEEE, 2020: 1577-1586. [24] ZHANG X Y, ZHOU X Y, LIN M X, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT,US:IEEE, 2018: 6848-6856. [25] LI X, SU J, YANG L. Building detection in SAR images based on bi-dimensional empirical mode decomposition algorithm [J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(4): 641-645. [26] HU J, SHEN L, ALBANIE S, et al. Squeeze-and- excitation networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023. [27] WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA,US:IEEE, 2020: 11531-11539. [28] HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[EB/OL].[2021-03-15]. https:∥dblp.org/rec/journals/corr/abs-2103-02907. [29] WANG J Q, CHEN K, XU R, et al. CARAFE: content-aware reassembly of features[C]∥Proceedings of IEEE International Conference on Computer Vision. Seoul, South Korea:IEEE, 2019: 3007-3016. [30] 李响, 苏娟, 杨龙. 基于改进YOLOV3的合成孔径雷达图像中建筑物检测算法[J]. 兵工学报, 2020, 41(7): 1347-1359. LI X, SU J, YANG L. A SAR image building detection algorithm based on improved YOLOV3 [J]. Acta Armamentarii, 2020, 41(7): 1347-1359. (in Chinese)
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