浏览全部资源
扫码关注微信
Published： 07 October 2022 ，
扫 描 看 全 文
许泽宇，沈占锋，李杨，李均力，王浩宇，李硕，焦淑慧，李苓苓.2022.结合E-YOLO和水体指数约束的大面幅影像水利设施检测.遥感学报，26（10）： 2083-2093
Xu Z Y，Shen Z F，Li Y，Li J L，Wang H Y，Li S，Jiao S H and Li L L. 2022. Detection of water conservancy facilities in large-format image combining E-YOLO algorithm and NDWI constraint. National Remote Sensing Bulletin， 26（10）：2083-2093
水利设施对水资源与水量调度、自然湿地生态保护与修复、资源和生态功能的利用及经济效益发展有重要作用。传统方法统计水利设施位置、数量等依赖于汇编资料，存在耗时长、资料更新不够及时以及具体地理位置不详等缺点，遥感为大规模监测水利设施提供了新的可能。本文以YOLO v3网络为基础，结合水利设施的特点，提出了一种基于大面幅影像快速检测水利设施的算法，主要分为两个方面：（1）改进的YOLO算法（E-YOLO）。E-YOLO提出PPA特征融合方法和等比预测框与四特征图交叉预测方法，对小样本等问题进行优化；改进损失函数，突出置信度损失；同时使用迁移学习的方法，读取特征提取部分的预训练模型参数。（2）基于E-YOLO算法和水体指数约束的大面幅水利设施检测算法。通过水体指数约束滑动步幅来解决影像面幅大、目标尺度小的问题，同时降低漏检率和误检率，再结合轮廓合并方法，优化检测结果。本研究中采用高分二号影像数据实现大面幅影像水利设施检测，实验结果表明：E-YOLO算法可以明显提高水利设施检测效果，相比平均F2精度相比YOLO v3提高了1.25%。且有更好的稳定性；水体指数约束的大面幅检测方法可以在保证效率的情况下提高检测精度，其F2精度相比大步幅和小步幅方法分别提高了3.72%和2.70%，为遥感水利设施检测提供了良好方案。
Water facilities play an important role in water scheduling
ecological protection and restoration of natural wetlands
utilization of resources and functions
and development. The traditional methods of statistics on the location and count of water conservancy facilities rely on compiled data
which has disadvantages such as time-consuming
untimely data update
and unknown specific geographic locations. Remote sensing provides new possibilities for large-scale detection of water conservancy facilities. Aiming at the problem of detection of water conservancy facilities with remote sensing images
this study proposes a large-scale image detection algorithm. Based on the YOLO v3 network and the characteristics of water conservancy facilities
the study was divided into two main aspects: (1) We improved the YOLO algorithm and obtained the E-YOLO algorithm. We proposed a PPA feature fusion method and a four-feature map cross prediction method with proportional prediction box to optimize the problems of small samples. Besides
we improved the loss function by highlighting the loss of confidence. In addition
we used the transfer learning method to read part of the feature extraction parameters of the pre-trained model. (2) With the improved E-YOLO algorithm as the core
a large-area water conservancy facility detection algorithm combined with the water body index constraint was obtained. Aiming at the problem of large image size with a small target scale
we used the water body index to constrain the sliding step to reduce the missed detection rate and false detection rate at the same time. Then we combined the network output with the contour merging method to optimize the detection results. We used the GF-2 data for this study. The experimental results show that: the E-YOLO algorithm can significantly improve the detection effect of water conservancy facilities. Compared with YOLO v3
the average F2 score of E-YOLO is increased by 1.25% and the E-YOLO algorithm has a better stability. The large-area detection method constrained by the water index can improve the detection accuracy while ensuring efficiency. Compared with the large-step and small-step methods
its F2 accuracy is increased by 3.72% and 2.70%
respectively. Our method provides a good solution for the detection of water conservancy facilities.
水利设施遥感检测E-YOLO大面幅影像NDWI
water conservancy facilitiesremote sensing detectionE-YOLOlarge imageNDWI
Audebert N, Le Saux B and Lefèvre S. 2018. Beyond RGB: very high resolution urban remote sensing with multimodal deep networks. ISPRS Journal of Photogrammetry and Remote Sensing, 140: 20-32 [DOI: 10.1016/j.isprsjprs.2017.11.011http://dx.doi.org/10.1016/j.isprsjprs.2017.11.011]
Fang H W and Shi H J. 2019. Satellite image recognition and classification method based on deep learning. Computer Systems & Applications, 28(10): 27-34
方浩文, 施华君. 2019. 基于深度学习的卫星图像识别分类方法. 计算机系统应用, 28(10): 27-34 [DOI: 10.15888/j.cnki.csa.007081http://dx.doi.org/10.15888/j.cnki.csa.007081]
Girshick R, Donahue J, Darrell T and Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH: IEEE: 580-587 [DOI: 10.1109/CVPR.2014.81http://dx.doi.org/10.1109/CVPR.2014.81]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE: 770-778 [DOI: 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90]
Hoeser T, Bachofer F and Kuenzer C. 2020. Object detection and image segmentation with deep learning on earth observation data: a review—part ii: applications. Remote Sensing, 12(18): 3053 [DOI: 10.3390/rs12183053http://dx.doi.org/10.3390/rs12183053]
Hong L, Huang Y J, Yang K, Peng S Y and Xu Q L. 2019. Study on urban surface water extraction from heterogeneous environments using GF-2 remotely sensed images. Journal of Remote Sensing, 23(5): 871-882
洪亮, 黄雅君, 杨昆, 彭双云, 许泉立. 2019. 复杂环境下高分二号遥感影像的城市地表水体提取. 遥感学报, 23(5): 871-882 [DOI: 10.11834/jrs.20198064http://dx.doi.org/10.11834/jrs.20198064]
Li H F, Dou X, Tao C, Wu Z X, Chen J, Peng J, Deng M and Zhao L. 2020. RSI-CB: a large-scale remote sensing image classification benchmark using crowdsourced data. Sensors, 20(6): 1594 [DOI: 10.3390/s20061594http://dx.doi.org/10.3390/s20061594]
Liu J G, Zang C F, Tian S Y, Liu J G, Yang H, Jia S F, You L Z, Liu B and Zhang M. 2013. Water conservancy projects in China: achievements, challenges and way forward. Global Environmental Change, 23(3): 633-643 [DOI: 10.1016/j.gloenvcha.2013.02.002http://dx.doi.org/10.1016/j.gloenvcha.2013.02.002]
Liu K, Ren H G, Li S Y and Qin B Y. 2019. Automatic extraction of Tibet Plateau frozen lake based on Tiangong-2 multi-spectral data. Infrared and Laser Engineering, 48(3): 40-46
刘康, 任海根, 李盛阳, 覃帮勇. 2019. 基于天宫二号多光谱数据的青藏高原冻湖自动提取. 红外与激光工程, 48(3): 40-46 [DOI: 10.3788/IRLA201948.0303004http://dx.doi.org/10.3788/IRLA201948.0303004]
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot multibox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer: 21-37 [DOI: 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2]
Luo J C, Sheng Y W, Shen Z F, Li J L and Gao L J. 2009. Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism. Journal of Remote Sensing, 13(4): 610-615
骆剑承, 盛永伟, 沈占锋, 李均力, 郜丽静. 2009. 分步迭代的多光谱遥感水体信息高精度自动提取. 遥感学报, 13(4): 610-615 [DOI: 10.11834/jrs.20090405http://dx.doi.org/10.11834/jrs.20090405]
Ma X, Shao L M, Jin X and Xu G L. 2019. Improved YOLO model and its application in ship target recognition. Telecommunication Engineering, 59(8): 869-874
马啸, 邵利民, 金鑫, 徐冠雷. 2019. 改进的YOLO模型及其在舰船目标识别中的应用. 电讯技术, 59(8): 869-874 [DOI: 10.3969/j.issn.1001-893x.2019.08.001http://dx.doi.org/10.3969/j.issn.1001-893x.2019.08.001]
McFeeters S K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Neubeck A and van Gool L. 2006. Efficient non-maximum suppression//Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong: IEEE: 850-855 [DOI: 10.1109/ICPR.2006.479http://dx.doi.org/10.1109/ICPR.2006.479]
Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE: 779-788 [DOI: 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91]
Redmon J and Farhadi A. 2017. YOLO9000: better, faster, stronger//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE: 6517-6525 [DOI: 10.1109/CVPR.2017.690http://dx.doi.org/10.1109/CVPR.2017.690]
Redmon J and Farhadi A. 2018. YOLOv3: an incremental improvement. arXiv: 1804.02767v1 [DOI: 10.48550/arXiv.1804.02767http://dx.doi.org/10.48550/arXiv.1804.02767]
Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149 [DOI: 10.1109/TPAMI.2016.2577031http://dx.doi.org/10.1109/TPAMI.2016.2577031]
Shen Z F, Xia L G, Li J L, Luo J C and Hu X D. 2013. Automatic and high-precision extraction of rivers from remotely sensed images with Gaussian normalized water index. Journal of Image and Graphics, 18(4): 421-428
沈占锋, 夏列钢, 李均力, 骆剑承, 胡晓东. 2013. 采用高斯归一化水体指数实现遥感影像河流的精确提取. 中国图象图形学报, 18(4): 421-428 [DOI: 10.11834/jig.20130409http://dx.doi.org/10.11834/jig.20130409]
Shi W Z, Caballero J, Huszár F, Totz J, Aitken A P, Bishop R, Rueckert D and Wang Z H. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE: 1874-1883 [DOI: 10.1109/CVPR.2016.207http://dx.doi.org/10.1109/CVPR.2016.207]
Tao Z Y. 2021. Detection and recognition of base in large-format remote sensing images. Huazhong University of Science & Technology
陶泽远. 2021. 大幅面遥感影像阵地目标检测与识别方法研究. 华中科技大学 [DOI: 10.27157/d.cnki.ghzku.2021.004412http://dx.doi.org/10.27157/d.cnki.ghzku.2021.004412]
Wang N, Cheng J H, Zhang H Y, Cao H J and Liu J. 2020. Application of U-net model to water extraction with high resolution remote sensing data. Remote Sensing for Land and Resources, 32(1): 35-42
王宁, 程家骅, 张寒野, 曹红杰, 刘军. 2020. U-net模型在高分辨率遥感影像水体提取中的应用. 国土资源遥感, 32(1): 35-42 [DOI: 10.6046 /gtzyyg.2020.01.06http://dx.doi.org/10.6046/gtzyyg.2020.01.06]
Wang X K, Jiang H X and Lin K Y. 2020. Remote sensing image ship detection based on modified YOLO algorithm. Journal of Beijing University of Aeronautics and Astronautics, 46(6): 1184-1191
王玺坤, 姜宏旭, 林珂玉. 2019. 基于改进型YOLO算法的遥感图像舰船检测. 北京航空航天大学学报, 46(6): 1184-1191 [DOI: 10.13700/j.bh.1001-5965.2019.0394http://dx.doi.org/10.13700/j.bh.1001-5965.2019.0394]
Wu J, Duan J, He L Q, Li Y C and Zhu W T. 2021. Research on aircraft detection algorithm of DS-YOLO network in remote sensing images. Computer Engineering and Applications, 57(1): 181-187
吴杰, 段锦, 赫立群, 李英超, 朱文涛. 2021. DS-YOLO网络在遥感图像中的飞机检测算法研究. 计算机工程与应用, 57(1): 181-187 [DOI: 10.3778/j.issn.1002-8331.1909-0409http://dx.doi.org/10.3778/j.issn.1002-8331.1909-0409]
Xu Y, Zhang Y J and Zhang Q. 2016. Rule sets building of water conservancy facilities spatial element extraction based on high resolution remote sensing image. Geospatial Information, 14(5): 71-74
许莹, 张友静, 张琴. 2016. 基于高分影像的水利空间要素提取规则集构建. 地理空间信息, 14(5): 71-74 [DOI: 10.3969/j.issn.1672-4623.2016.05.023http://dx.doi.org/10.3969/j.issn.1672-4623.2016.05.023]
Zhang Z X. 2018. Fast Detection of Aircraft Target in Large-Scale Optical Remote Sensing Image. Changchun: University of Chinese Academy of Sciences (Changchun Institute of Optics, Fine Mechanics, Chinese Academy of Sciences)
张作省. 2018. 大面幅光学遥感影像飞行器目标快速检测. 长春: 中国科学院大学(中国科学院长春光学精密机械与物理研究所)
Zheng Z Q, Liu Y Y, Pan C C and Li G N. 2019. Application of improved YOLO v3 in aircraft recognition of remote sensing images. Electronics Optics and Control, 26(4): 28-32
郑志强, 刘妍妍, 潘长城, 李国宁. 2019. 改进YOLO V3遥感图像飞机识别应用. 电光与控制, 26(4): 28-32 [DOI: 10.3969/j.issn.1671-637X.2019.04.006http://dx.doi.org/10.3969/j.issn.1671-637X.2019.04.006]
Zhou L L. 2010. Study on water resource market allocation in Turim river basin. Xinjiang Agricultural University
周莉荔. 2010. 塔里木河流域水资源市场配置研究. 新疆农业大学
相关文章
相关作者
相关机构
京公网安备11010802024621