一种高分辨率遥感图像视感知目标检测算法

李策; 张亚超; 蓝天; 杜少毅

doi:10.7652/xjtuxb201806002

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一种高分辨率遥感图像视感知目标检测算法

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- 一种高分辨率遥感图像视感知目标检测算法
- An Object Detection Algorithm with Visual Perception for High-Resolution Remote Sensing Images
- 西安交通大学学报 2018年52卷第6期页码：9-16
- 作者机构：
- 作者简介：
- 基金信息：
  
  : 2017-09-20。作者简介: 李策(1974—),男,教授;杜少毅(通信作者),男,副教授。基金项目: 国家自然科学基金资助项目(61365003,61573274);甘肃省基础研究创新群体计划资助项目(1506RJIA031);西安交通大学基本科研业务费综合交叉类资助项目(xjj2017005)
- DOI：10.7652/xjtuxb201806002
  中图分类号： TP751.1
- 网络首发：2018-06-10，
  
  纸质出版：2018
- 稿件说明：
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李策, 张亚超, 蓝天, 等. 一种高分辨率遥感图像视感知目标检测算法[J]. 西安交通大学学报, 2018,52(6):9-16.

An Object Detection Algorithm with Visual Perception for High-Resolution Remote Sensing Images[J]. 2018, 52(6): 9-16.
李策, 张亚超, 蓝天, 等. 一种高分辨率遥感图像视感知目标检测算法[J]. 西安交通大学学报, 2018,52(6):9-16. DOI： 10.7652/xjtuxb201806002.

An Object Detection Algorithm with Visual Perception for High-Resolution Remote Sensing Images[J]. 2018, 52(6): 9-16. DOI： 10.7652/xjtuxb201806002.

摘要

针对大幅面高分辨率光学遥感图像目标检测尚存在着检测精度和效率低的问题

提出了一种高分辨率遥感图像视感知目标检测算法。该算法首先通过显著区域有选择性的引导获取场景中的子区域

将计算资源转移到可能包含目标的区域中

以降低计算复杂度; 然后

利用基于单次检测器(YOLO)卷积神经网络目标检测模型获取预选目标; 最后

提出目标语义关联抑制对获取的预选目标进行筛选得到有效目标

能够减少虚假目标的干扰

降低虚警率。所提算法在公开NWPU_VHR-10数据集上的平均检测精度为0.865

高于对比算法

在包含更多高分辨率的LUT_VHRVOC-2数据集上

比YOLO的检测效果更好。实验结果表明

所提算法提高了大幅面高分辨率遥感图像的目标检测精度。

Abstract

An effective object detection approach with visual perception for high-resolution remote sensing images is proposed to address the problem that the accuracy and speed of existing object detection algorithms of remote sensing images are low

especially in large-scale and high-resolution remote sensing images. Firstly

some sub-regions are selected in the scene using a saliency map

and then transfer computing resources to the area that may contain objects to reduce the computational complexity. Then

pre-selected objects are obtained by a fast learning model YOLO(you only look once). An object semantic association suppress is proposed to screen the pre-selected objects for effective objects. It reduces the interference of false objects for reducing the false alarm probability. Experimental results on NWPU_VHR-10 dataset show that the proposed algorithm is the best

and the mean average precision(mAP)is 86.53%. The results of the proposed algorithm are much better than those of YOLO on LUT_VHRVOC-2 dataset which contains more large-scale and high-resolution remote sensing images. It is concluded that the performance of the high-resolution remote sensing image is improved by the proposed algorithm.

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Keywords

references

FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model [C]∥Proceedings of the IEEE Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2008: 1-8.

AHMADI S, ZOEJ M J V, EBADI H, et al. Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours [J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12(3): 150-157.

李亚超, 周瑞雨, 全英汇, 等. 采用自适应背景窗的舰船目标检测算法 [J]. 西安交通大学学报, 2013, 47(6): 25-30.

LI Yachao, ZHOU Ruiyu, CHUAN Yinghui, et al. A ship detection algorithm of adaptive background window [J]. Journal of Xi'an Jiaotong University, 2013, 47(6): 25-30.

杜春, 孙即祥, 李智勇, 等. 光学遥感舰船目标识别方法 [J]. 中国图象图形学报, 2012, 17(4): 589-595.

DU Chun, SUN Jixiang, LI Zhiyong, et al. Ship target recognition method for optical remote sensing [J]. Journal of Image and Graphics, 2012, 17(4): 589-595.

XU Sheng, FANG Tao, LI Deren, et al. Object classification of aerial images with bag-of-visual words [J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(2): 366-370.

CHEN Yi, NASRABADI N M, TRAN T D. Sparse representation for target detection in hyperspectral imagery [J]. IEEE Selected Topics in Signal Processing, 2011, 5(3): 629-640.

GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]∥ Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2014: 580-587.

REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2017, 39(6): 1137-1149.

REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2016: 779-788.

LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector [C]∥Proceedings of the 14th European Conference on Computer Vision. Berlin, Germany: Springer Verlag, 2016: 21-37.

CHENG Gong, ZHOU Peicheng, HAN Junwei. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images [J]. IEEE Geoscience and Remote Sensing, 2016, 54(12): 7405-7415.

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2012, 60(6): 84-90.

高常鑫, 桑农. 基于深度学习的高分辨率遥感影像目标检测 [J]. 测绘通报, 2014(s1): 108-111.

GAO Changxin, SANG Nong. Object detection of high resolution remote sensing image based on the deep study [J]. Bulletin of Surveying and Mapping, 2014(s1): 108-111.

ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis [J]. IEEE Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.

UIJLINGS J R R, SANDE K E A, GEVERS T, et al. Selective search for object recognition [J]. International Journal of Computer Vision, 2013, 104(2): 154-171.

CHENG Gong, HAN Junwei, ZHOU Peicheng, et al. Multi-class geospatial object detection and geographic image classification based on collection of part detectors [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 98: 119-132.

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