基于多尺度视觉特征组合的高分遥感影像目标检测

doi:10.3969/j.issn.0255-8297.2018.03.007

应用科学学报 ›› 2018, Vol. 36 ›› Issue (3): 471-484.doi: 10.3969/j.issn.0255-8297.2018.03.007

基于多尺度视觉特征组合的高分遥感影像目标检测

冯发杰¹, 吏军平¹, 丁亚洲¹, 朱坤², 崔卫红²

1. 湖北省电力勘测设计院, 武汉 430040;
2. 武汉大学遥感信息工程学院, 武汉 430079

收稿日期:2017-01-02 修回日期:2017-03-05 出版日期:2018-05-31 发布日期:2018-05-31
通信作者: 崔卫红,副教授,研究方向:高分遥感影像信息提取与变化检测等,E-mail:whcui@whu.edu.cn E-mail:whcui@whu.edu.cn
基金资助:
国家自然科学基金（No.41101410）资助

Target Detection from High Resolution Remote Sensing Images Based on Combination of Multi-scale Visual Features

FENG Fa-jie¹, LI Jun-ping¹, DING Ya-zhou¹, ZHU Kun², CUI Wei-hong²

1. Hubei Electric Engineering Corporation, Wuhan 430040, China;
2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Received:2017-01-02 Revised:2017-03-05 Online:2018-05-31 Published:2018-05-31

摘要/Abstract

摘要：

针对高空间分辨率（高分）遥感影像中目标空间结构明显、颜色丰富的特点，提出一种基于多尺度视觉特征组合的目标检测方法.首先采用两组特征描述子来表达目标的颜色与空间结构特征，其中，一组是彩色变换后的密集空间金字塔尺度不变特征变换（scale-invariantfeature transform，SIFT）描述子，另一组是多尺度方向梯度直方图（histogram of orientedgradient，HOG）与色度、饱和度、明度（hue，saturation and value，HSV）颜色模型组合的特征描述子，然后用支持向量机（support vector machine，SVM）对两组特征分别进行模型训练与目标检测，对于彩色变换密集空间金字塔SIFT描述子，采用视觉词袋空间金字塔匹配核（spatial pyramid matching kernel，SPMK）构建SVM的输入，对于HOG与HSV特征，直接将特征向量按顺序合成作为SVM的输入，最后取两组检测结果的交集作为最终检测结果.为验证方法的有效性，分别以航空影像中的建筑物和鱼排，以及高分卫星影像中的游艇为目标进行检测.结果表明，该方法的目标检测精度可达到90%以上.

关键词: 多尺度视觉特征, 目标检测, 支持向量机

Abstract:

To utilize the spatial structure and color information of the target in the high resolution remote sensing images, this paper proposes a method of target detection based on multi-scale visual feature combination. Firstly, we use two types of feature descriptor to express the color and spatial structure information of targets, one is the dense spatial pyramid scale-invariant feature transform (SIFT) descriptor in spatial pyramid of the color transformed images, the other is the multi-scale histogram of oriented gradient (HOG) feature combined with the hue, saturability and value (HSV) features. Secondly, the two sets of features are used to train classifer through support vector machine (SVM). For the color transformed dense SIFT spatial pyramid feature descriptor, the input of SVM is constructed by spatial pyramid matching kernel (SPMK). For the combined HOG and HSV feature, the orderly-combined eigenvectors are taken as the input of the SVM classifer. Finally the intersection of the two classifcation results are regarded as the fnal results. For verifcation, we tested the buildings and fsh rafts in aerial images and the yachts in satellite images. Experimental results show that the proposed method can improve the precision of target detection, which can reach to by 90% or better.

Key words: target detection, multi-scale visual feature, support vector machine (SVM)

中图分类号:

TP79

冯发杰, 吏军平, 丁亚洲, 朱坤, 崔卫红. 基于多尺度视觉特征组合的高分遥感影像目标检测[J]. 应用科学学报, 2018, 36(3): 471-484.

FENG Fa-jie, LI Jun-ping, DING Ya-zhou, ZHU Kun, CUI Wei-hong. Target Detection from High Resolution Remote Sensing Images Based on Combination of Multi-scale Visual Features[J]. Journal of Applied Sciences, 2018, 36(3): 471-484.

参考文献

[1] Blaschke T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2010, 65(1):2-16.
[2] 周军其,李志娟. 空间关系辅助的面向对象建筑物提取[J]. 应用科学学报,2012, 30(5):511-526. Zhou J Q, Li Z J. Spatial relation-aided method for object-oriented extraction of buildings from high resolution image[J]. Journal of Applied Sciences, 2012, 30(5):511-526. (in Chinese)
[3] 何少林,徐京华,张帅毅. 面向对象的多尺度无人机影像土地利用信息提取[J]. 国土资源遥感,2013, 25(2):107-112. He S L, Xu J H, Zhang S Y. Land use classifcation of object-oriented multi-scale by UAV image[J]. Remote Sensing for Land and Resources, 2013, 25(2):107-112. (in Chinese)
[4] 任慧,谈亚光. 基于面向对象的国情普查地表覆盖提取方法[J]. 地理空间信息,2014, 12(3):14-16. Ren H, Tan Y G. Object-oriented surface coverage extract methods of geographic situation census[J]. Geospatial Information, 2014, 12(3):14-16. (in Chinese)
[5] 汤育红. 地理国情普查地表覆盖与国情要素信息的提取方法探讨[J]. 测绘与空间地理信息,2013, 36(12):89-91. Tang Y H. Geographical conditions census surface coverage with the state of the element information extraction method to explore[J]. Geomatics & Spatial Information Technology, 2013, 36(12):89-91. (in Chinese)
[6] 翟亮,张晓贺,桑会勇. 面向地理国情普查的地表覆盖分类技术与试验[J]. 遥感信息,2014(4):71-75. Zhai L, Zhang X H, Sang H Y. Land cover classifcation for national geographic conditions gensu[J]. Remote Sensing Information, 2014(4):71-75. (in Chinese)
[7] 黄慧萍. 面向对象影像分析中的尺度问题研究[D]. 北京:中国科学院遥感应用研究所,2003.
[8] 林先成,李永树. 成都平原高分辨率遥感影像分割尺度研究[J]. 国土资源遥感,2010, 2010(2):7-11. Lin X C, Li Y S. A study of the segmentation scale of high-resolution remotely sensed data in Chengdu plain[J]. Remote Sensing for Land & Resources, 2010(2):7-11. (in Chinese)
[9] Meng Y, Lin C, Cui W. Scale selection based on Moran's I for segmentation of high resolution remotely sensed images[C]//IGARSS IEEE International Geoscience & Remote Sensing Symposium. IEEE, 2014:4895-4898.
[10] Sivic J, Zisserman A. Video Google:a text retrieval approach to object matching in videos[C]//Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, France, 2003:1470-1477.
[11] Lazebnik S, Schmid C, Ponce J. Beyond bags of feature:spatial pyramid matching for recognizing natural scene categories[C]//Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). New York:IEEE, 2006:2169-2178.
[12] Yang Y, Newsam S. Spatial pyramid co-occurrence for image classifcation[J]. IEEE International Conference on Computer Vision, 2011, 23(5):1465-1472.
[13] Mazinan A H, Sarikhani M. Providing an efcient intelligent transportation system through detection, tracking and recognition of the region of interest in trafc signs by using non-linear SVM classifer in line with histogram oriented gradient and Kalman flter approach[J]. Sadhana. 2014, 39(1):27-37.
[14] Moranduzzo T, Melgani F. Detecting cars in UAV images with a catalog-based approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10):6356-6367.
[15] Lowe D G. Object recognition from local scale-invariant features[C]//The proceedings of the seventh IEEE international conference on Computer vision, 1999(2):1150-1157.
[16] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
[17] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005:886-893.
[18] Sande K E A, Gevers T, Snoek C G M. Evaluating color descriptors for object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010, 32(9):1582-1596.
[19] Corinna C, Vladimir V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273- 297.
[20] Grauman, K. Darrell, T. The pyramid match kernel:discriminative classifcation with sets of image features[C]//10th IEEE International Conference on Computer Vision (2005 ICCV), 2005(2):1458-1465.

基于多尺度视觉特征组合的高分遥感影像目标检测

Target Detection from High Resolution Remote Sensing Images Based on Combination of Multi-scale Visual Features

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	迟楠, 牛文清, 贾俊连, 哈依那尔. 基于抗非线性SVM的几何整形可见光通信系统[J]. 应用科学学报, 2020, 38(4): 647-658.
[2]	张彤彤, 董军宇, 赵浩然, 李琼, 孙鑫. 基于知识蒸馏的轻量型浮游植物检测网络[J]. 应用科学学报, 2020, 38(3): 367-376.
[3]	范书瑞, 贾雅亭, 刘晶花. 基于三轴加速度传感器人体姿态识别的特征选择[J]. 应用科学学报, 2019, 37(3): 427-436.
[4]	苏文煊, 方针. 基于CFA插值特性不一致的图像真伪鉴别[J]. 应用科学学报, 2019, 37(1): 33-40.
[5]	许忠雄, 邵瑰玮, 谢予星, 吴亮, 季铮. 基于编码标志的无人机电力巡检自主定位方法[J]. 应用科学学报, 2018, 36(5): 845-858.
[6]	杨晋生, 林振军. 一种改进支持向量机的无线定位方法[J]. 应用科学学报, 2017, 35(6): 685-692.
[7]	翟旭平, 孟田, 王涛. 采用对数预处理的SVM频谱感知方法[J]. 应用科学学报, 2017, 35(6): 726-734.
[8]	熊邦书, 吴强镪, 李新民, 莫燕, 黄建萍. 基于频段能量特征的低速滚动轴承故障诊断[J]. 应用科学学报, 2017, 35(3): 366-372.
[9]	张过. 卫星视频处理与应用进展[J]. 应用科学学报, 2016, 34(4): 361-370.
[10]	熊邦书, 刘雨, 莫燕, 黄建萍, 李新民. 基于SVM的直升机飞行状态识别[J]. 应用科学学报, 2016, 34(4): 469-474.
[11]	胡昭华, 徐玉伟, 赵孝磊, 何军, 周游. 基于支持向量机的多特征选择目标跟踪[J]. 应用科学学报, 2015, 33(5): 502-517.
[12]	杨洪涛，耿金华，丁小瑞，喻曹丰，禹斌. 数控机床力-几何误差的PSO-SVM建模[J]. 应用科学学报, 2014, 32(3): 325-330.
[13]	王国力，周伟，丛瑜，关键. 多尺度自卷积方差显著性SAR图像目标检测[J]. 应用科学学报, 2013, 31(6): 607-6.
[14]	吴量1，江桦1，崔伟亮1,2. 利用循环平稳性检测和支持向量机的调制信号分类[J]. 应用科学学报, 2013, 31(6): 593-600.
[15]	温祥西，孟相如，李明迅. 基于最优样本子集的在线模糊LSSVM混沌时间序列预测[J]. 应用科学学报, 2013, 31(4): 411-417.