窗口融合特征对比度的光学遥感目标检测

李湘眷; 王彩玲; 李宇; 孙皓

doi:10.3788/OPE.20162408.2067

您当前的位置：

首页 >

文章列表页 >

窗口融合特征对比度的光学遥感目标检测

信息科学 | 更新时间：2020-07-07

- 窗口融合特征对比度的光学遥感目标检测
- Optical remote sensing object detection based on fused feature contrast of subwindows
- Editorial Office of Optics and Precision Engineeri 2016年24卷第8期页码：2067-2077
- 作者机构：
  
  1. 西安石油大学, 陕西西安 710065
  2. 中国科学院遥感与数字地球研究所, 北京 100942
  3. 中国科学院空间信息处理与应用系统技术重点实验室, 北京 100190
  4. 中国科学院电子学研究所, 北京 100190
- 作者简介：
  
  李湘眷(1984-)，女，陕西西安人，博士，讲师，2006年于西安电子科技大学获得学士学位，2012年于北京中国科学院电子学研究所获得博士学位，主要从事遥感图像处理与模式识别方面的科研和教学工作。E-mail：xiangjuan_li@126.com xiangjuan_li@126.com
  [ "王彩玲(1984-)，女，宁夏吴忠人，博士，讲师，2006年于天津大学获得学士学位，2011年于中国科学院西安光学精密机械研究所获得博士学位，主要从事遥感影像数据处理技术方面的科研和教学工作。E-mail：azering@163.com " ]
- 基金信息：
  
  国家自然科学基金资助项目(No.41301480，No.41301382);陕西省自然科学基础研究计划资助项目(No.2014JQ5181);陕西省教育厅专项科研计划资助项目(No.14JK1573)
- DOI：10.3788/OPE.20162408.2067
  中图分类号： TP751
- 收稿日期：2016-03-21，
  
  录用日期：2016-7-3，
  
  纸质出版日期：2016-08-25
- 稿件说明：
移动端阅览
李湘眷, 王彩玲, 李宇, 等. 窗口融合特征对比度的光学遥感目标检测[J]. Editorial Office of Optics and Precision Engineeri, 2016,24(8):2067-2077.

Xiang-juan LI, Cai-ling WANG, Yu LI, et al. Optical remote sensing object detection based on fused feature contrast of subwindows[J]. Optics and precision engineering, 2016, 24(8): 2067-2077.
李湘眷, 王彩玲, 李宇, 等. 窗口融合特征对比度的光学遥感目标检测[J]. Editorial Office of Optics and Precision Engineeri, 2016,24(8):2067-2077. DOI： 10.3788/OPE.20162408.2067.

Xiang-juan LI, Cai-ling WANG, Yu LI, et al. Optical remote sensing object detection based on fused feature contrast of subwindows[J]. Optics and precision engineering, 2016, 24(8): 2067-2077. DOI： 10.3788/OPE.20162408.2067.

摘要

提出了一种基于窗口融合特征对比度的光学遥感目标检测方法。首先，在训练图像上生成大量不同尺寸的滑动窗，计算了各窗口的多尺度显著度、仿射协变区域对比度、边缘密度对比度以及超像素完整度4项特征分值，在确认集上基于窗口重合度和后验概率最大化学习各个特征的阈值参数。然后，采用Naive Bayes框架进行特征融合，并训练分类器。在目标检测阶段首先计算测试图像中各窗口的多尺度显著度分值，初步筛选出显著度高且符合待检测目标尺寸比例的部分窗口。然后计算初选窗口集的其余3项特征，再根据训练好的分类模型计算各个窗口的后验概率。最后，挑选出局部高分值的候选区域并进行判断合并，得到最终目标检测结果。针对飞机、油罐、舰船等3类遥感目标的检测结果显示：4类特征在单独描述3类目标时表现出的性能各有差异，最高检测准确率为74.21%~80.32%，而融合方案能够综合考虑目标自身特点，准确率提高至80.78~87.30%。与固定数量滑动窗方法相比，准确率从约80%提高到约85%，虚警率从20%左右降低为3%左右。最终高分值区域数降低约90%，测试时间减少约25%。得到的结果显示该方法大大提高了目标检测精度和算法效率。

Abstract

A detection algorithm for optical remote sensing targets was proposed based on the fused features contrast of subwindows. Firstly

a large number of varisized sliding windows were generated in a training image

and four types of scores related to multi-scale saliency

affine invariant region contrast

edge density and superpixel straddling were computed within each window. The feature parameters were learned on validation sets by maximizing localization accuracy and posterior probability. Then

all the features were combined in a Naive Bayesian framework and a classifier was trained. In the target detection step

the multi-scale saliency score was firstly computed within all the windows of test images

and partial windows with higher saliency and proper sizes matching to the objects to be detected were selected preliminarily. Furthermore

other scores were computed within the selected windows

and the posterior probability of each window was computed by using the trained classifier. Finally

windows with high local scores were selected and merged and the final detection results were obtained. The detection experiments were performed on three types of remote targets including planes

oilcans and ships

and the results show that each type of feature appears different properties for targets described

the highest accuracy is 74.21% to 80.32%. The proposed method outperforms all the single feature methods and the accuracy is improved to 80.87% to 87.30%. By compared with the fixed number sliding window algorithm

the accuracy rate is improved from about 80% to 85% and the false alarm rate is reduced from about 20% to 3%. Furthermore

the proposed method shows a 90% reduction in the number of windows and 25% reduction in the detection time due to the selection in the intermediary stage. It concludes that the method improves detection accuracy and algorithm efficiency greatly.

关键词

Keywords

references

DESAI C, RAMANNAN D, FOLKESS C. Discriminative models for multi-class object layout [J]. International Journal of Computer Vision, 2011, 95(1): 1-12.

FELZENSZWALB P F, GIRSHICK R B, DAVID M A, et al. Object detection with discriminatively trained part-based models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.

LONG C, WANG X, HUA G, et al. Accurate object detection with location relaxation and regionlets relocalization [C]. Asian Conference on Computer Vision, Singapore, Nov 1-5, 2014: 260-275.

LAMPERT C H, BLASCHKO M B, HOFMANN T. Beyond sliding windows: Object localization by efficient subwindow search [C]. 2008 IEEE Conference on Computer Vision & Pattern Recognition, USA, June 24-26, 2008: 1-8. http://www.oalib.com/references/16901887

ZHANG S, XIE M. Beyond sliding windows: Object detection based on hierarchical segmentation model [C]. IEEE International Conference on Communications, Hungary, Jun 9-13, 2013: 263-266.

张志龙, 杨卫平, 张淼, 等. 基于频谱残留变换的红外遥感图像舰船目标检测方法[J]. 电子与信息学报, 2015, 37(9): 2144-2150.

ZHANG ZH L, YANG W P, ZHANG M, et al. Ship detection in infrared remote sensing images based on spectral residual transform [J]. Journal of Electronics & Information Technology, 2015, 37(9): 2144-2150. (in Chinese)

高立宁, 毕福昆, 龙腾, 等. 一种光学遥感图像海面舰船检测算法[J]. 清华大学学报(自然科学版), 2011, 51(1): 105-110.

GAO L N, BI F K, LONG T, et al. Ship detection algorithm for optical remote sensing images [J]. J Tsinghua Univ(Sci & Tech), 2011, 51(1): 105-110. (in Chinese)

MOHAMMAD A, HOSSEIN E. Accurate object detection using local shape descriptors [J]. Pattern Analysis and Application, 2015, 18(2): 277-295.

WANG X, LV Q, WANG B, et al. Airport detection in remote sensing images: a method based on saliency map [J]. Cognitive Neurodynamics, 2013, 7(2): 143-154.

聂海涛, 龙科慧, 马军, 等. 采用改进尺度不变特征变换在多变背景下实现快速目标识别[J]. 光学精密工程, 2015, 23(8): 2349-2355.

NIE H T, LONG K H, MA J, et al. Fast object recognition under multiple varying background using improved SIFT method [J]. Opt. Precision Eng., 2015, 23(8): 2349-2355. (in Chinese)

周姣, 辛云宏. 基于显著性与尺度空间的红外弱小目标检测[J]. 激光与红外, 2015, 45(4): 452-456.

ZHOU J, XIN Y H. Infrared dim small target detection based on saliency and scale-space [J]. Laser & Infrared, 2015, 45(4): 452-456. (in Chinese)

张宇, 何楚, 石博, 等. 逐层特征选择的多层部件模型用于遥感图像飞机目标检测[J]. 武汉大学学报(信息科学版), 2014, 39(12): 1406-1411.

ZHANG Y, HE CH, SHI B, et al. Multi-layer feature selection based hierarchal component model for aero-plane detection on remote sensing image [J]. Geomatics and Information Science of Wuhan University, 2014, 39(12): 1406-1411. (in Chinese)

安彧, 王小非, 夏学知, 等. 海战场光学遥感图像舰船目标检测[J]. 武汉大学学报(工学版), 2015, 48(4): 568-573.

AN H, WANG X F, XIA X ZH, et al. Detection of sea battlefield's ship targets in optical remote sensing images [J]. Engineering Journal of Wuhan University, 2015, 48(4): 568-573. (in Chinese)

LI Y, WANG S, TIAN Q, et al. Learning cascaded shared-boost classifiers for part-based object detection [J]. IEEE Transactions on Image Processing, 2014, 23(4): 1858-1871.

LEIBE B, LEONARDIS A, SCHIELE B. Robust object detection with interleaved categorization and segmentation [J]. International Journal of Computer Vision, 2015, 77(1-3): 259-289.

李平, 魏仲慧, 何昕, 等. 采用多形状特征融合的多视点目标识别[J]. 光学精密工程, 2014, 22(12): 3368-3376.

LI P, WEI ZH H, HE X, et al. Object recognition based on shape fearure fusion under multi-views [J]. Opt. Precision Eng., 2014, 22(12): 3368-3376. (in Chinese)

谭熊, 余旭初, 张鹏强, 等. 基于多核支持向量机的高光谱影像非线性混合像元分解[J]. 光学精密工程, 2014, 22(7): 1912-1920.

TAN X, YU X CH, ZHANG P Q, et al. NonLinear mixed pixel decomposition of hyperspectral imagery based on multiple kernel SVM [J]. Opt. Precision Eng., 2014, 22(7): 1912-1920. (in Chinese)

杜杰, 吴谨, 朱磊. 基于区域特征融合的RGBD显著目标检测[J]. 液晶与显示, 2016, 31(1): 117-123.

DU J, WU J, ZHU L, RGBD salient object detection based on regional feature integration[J]. Chinese Journal of Liquid Crystals and Displays, 2016, 31(1): 117-123. (in Chinese)

邓丹, 吴谨, 朱磊, 等. 基于纹理抑制和连续分布估计的显著性目标检测方法[J]. 液晶与显示, 2015, 30(1): 120-125.

DENG D, WU J, ZHU L, et al. Significant target detection method based on texture inhibition and continuous distribution estimation [J]. Chinese Journal of Liquid Crystals and Displays, 2015, 30(1): 120-125. (in Chinese)

HOU X, ZHANG L. Saliency detection: A spectral residual approach[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-8. http://www.oalib.com/references/19318745

MIKOLAJCZYK K, SEHMID C. Scale & affine invariant interest point detectors [J]. International Journal on Computer Vision, 2004, 60(1): 63-86.

ALEXE B, DESELAERS T, FERRARI V. Measuring the objectness of image windows [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2189-2202.

MARTIN D, FOWLKES C, MALIK J. Learning to detect natual image boundaries using local brightness, color, and textures cues[J]. PAMI, 2004, 26(5): 530-549.

FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation[J]. International Journal on Computer Vision, 2004, 59(2): 167-181.

孙显, 王宏琦, 杨志峰. 基于形状统计模型的多类目标自动识别方法[J]. 电子与信息学报, 2009, 31(11): 2626-2631.

SUN X, WANG H Q, YANG ZH F. Automatic multi-categorical objects recognition using shape statistical models[J]. Journal of Electronics & Information Technology, 2009, 31(11): 2626-2631. (in Chinese)

浏览量

348

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

电力巡检中的偏振图像特征融合

超融合残差行进几何感知的遥感目标检测

半监督式野生动物夜间目标端到端检测

基于金字塔边缘增强的自矫正低光照目标检测

基于影像视锥的二维三维一体雷视融合车速测量