一种多时相遥感图像多目标检测算法

doi:10.7523/j.issn.2095-6134.2021.04.009

中国科学院大学学报 ›› 2021, Vol. 38 ›› Issue (4): 503-510.DOI: 10.7523/j.issn.2095-6134.2021.04.009

一种多时相遥感图像多目标检测算法

席彦新^1,2, 计璐艳¹, 耿修瑞^1,2

1. 中国科学院空天信息创新研究院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 100094;
2. 中国科学院大学, 北京 100049

收稿日期:2019-10-29 修回日期:2020-03-27 发布日期:2021-07-10
通讯作者: 计璐艳
基金资助:
国防科工局高分重大专项（06-Y20A17-9001-17/18，30-Y20A15-9003-17/18，30-Y20A28-9004-15/17）和国家自然科学基金委重大科研仪器研制项目（41427805）资助

An algorithm for multi-target detection in multi-temporal remote sensing images

XI Yanxin^1,2, JI Luyan¹, GENG Xiurui^1,2

1. Key Laboratory of Technology in Geospatial Information Processing and Application System of CAS, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-10-29 Revised:2020-03-27 Published:2021-07-10

摘要/Abstract

摘要： 随着遥感技术的快速发展，遥感图像得到广泛的应用，其中目标检测是遥感图像众多应用中非常重要的一个分支。目前大多数目标检测算法都是基于单时相遥感图像，对于多时相遥感数据的处理方法较少。最近发表的多时相目标检测算法有滤波张量分析，其将多重线性函数与张量相对应的关系应用于遥感图像目标检测，但该算法只能进行单目标检测，无法同时检测多个目标。本文借鉴多目标约束能量最小化算法中对于多个目标输出能量的约束和滤波张量分析中对于应用张量滤波器的思想，提出多目标滤波张量分析算法，能够在多时相遥感数据中实现同时检测多个目标。模拟和真实数据实验结果均表明，该算法可以有效地提高多时相图像中多目标检测精度。

关键词: 多目标检测, 多时相, 滤波张量分析, 约束能量最小化, 遥感图像

Abstract: With the rapid development of remote sensing technology, the remote sensing data become valuable in many practical applications. Among them, target detection has always been an important topic. However, most of the target detection algorithms in remote sensing images merely concentrate on single-temporal data, and there are few algorithms for multi-temporal data. In the field of target detection in multi-temporal remote sensing data, filter tensor analysis (FTA) has achieved great success which outperforms other target detection algorithms for single-temporal data. Yet FTA is designed only for single target detection, which means it can not meet the need for practical applications in circumstances where it has to detect more than one target simultaneously. So, in this paper, a modified algorithm for multi-target detection in multi-temporal data has been proposed based on the target constraints in multiple target constrained energy minimization and the tensor filter in FTA. Both the experiment results on simulation data and real remote sensing data from Landsat 8 prove that the algorithm proposed in this paper can effectively detect several targets in multi-temporal data.

Key words: multi-target detection, multi-temporal, filter tensor analysis, constrained energy minimization, remote sensing data

中图分类号:

TP753

席彦新, 计璐艳, 耿修瑞. 一种多时相遥感图像多目标检测算法[J]. 中国科学院大学学报, 2021, 38(4): 503-510.

XI Yanxin, JI Luyan, GENG Xiurui. An algorithm for multi-target detection in multi-temporal remote sensing images[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(4): 503-510.

参考文献

[1] Han J W, Zhang D W, Cheng G, et al. Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(6):3325-3337.
[2] Liu W, Yamazaki F, Vu T T. Automated vehicle extraction and speed determination from quickbird satellite images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(1):75-82.
[3] Manolakis D, Marden D, Shaw G A. Hyperspectral image processing for automatic target detection applications[J]. Lincoln Laboratory Journal, 2003, 14(1):79-116.
[4] Manolakis D, Shaw G. Detection algorithms for hyperspectral imaging applications[J]. IEEE Signal Processing Magazine, 2002, 19(1):29-43.
[5] Kraut S, Scharf L L, Butler R W. The adaptive coherence estimator:a uniformly most-powerful-invariant adaptive detection statistic[J]. IEEE Transactions on Signal Processing, 2005, 53(2):427-438.
[6] Harsanyi J C. Detection and classification of subpixel spectral signatures in hyperspectral image sequences[D]. University of Maryland, Baltimore County, 1993.
[7] Geng X R, Ji L Y, Sun K. Clever eye algorithm for target detection of remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114:32-39.
[8] Narumalani S, Mishra D R, Burkholder J, et al. A comparative evaluation of ISODATA and spectral angle mapping for the detection of saltcedar using airborne hyperspectral imagery[J]. Geocarto International, 2006, 21(2):59-66.
[9] Du Q, Ren H, Chang C I. A comparative study for orthogonal subspace projection and constrained energy minimization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6):1525-1529.
[10] Harsanyi J C, Chang C I. Hyperspectral image classification and dimensionality reduction:an orthogonal subspace projection approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4):779-785.
[11] 高仕博, 程咏梅, 赵永强, 等. 基于多时相红外图像探测浅层地下目标[J]. 红外与毫米波学报, 2009, 28(1):25-30.
[12] Xiao X M, Boles S, Liu J Y, et al. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data[J]. Remote Sensing of Environment, 2002, 82(2/3):335-348.
[13] Usman M, Liedl R, Shahid M A, et al. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data[J]. Journal of Geographical Sciences, 2015, 25(12):1479-1506.
[14] Xiao X M, Boles S, Liu J Y, et al. Mapping paddy rice agriculture in Southern China using multi-temporal MODIS images[J]. Remote Sensing of Environment, 2005, 95(4):480-492.
[15] Fichera C R, Mehmet G, Polino M. Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics[J]. European Journal of Remote Sensing, 2012, 45(1):1-18.
[16] Geng X R, Ji L Y, Zhao Y C. Filter tensor analysis:a tool for multi-temporal remote sensing target detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151:290-301.
[17] Ren H, Du Q, Chang C I, et al. Comparison between constrained energy minimization based approaches for hyperspectral imagery[C]//IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Greenbelt, MD, USA:IEEE Press, 2003:244-248.
[18] 尹继豪, 王艳, 王义松. 一种改进的高光谱图像中多小目标检测算法[J]. 电子学报, 2010, 38(9):1975-1978.
[19] 张贤达. 矩阵分析与应用[M].2版. 北京:清华大学出版社, 2013.
[20] Youden W J. Index for rating diagnostic tests[J]. Cancer, 1950, 3(1):32-35.

一种多时相遥感图像多目标检测算法

An algorithm for multi-target detection in multi-temporal remote sensing images

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

访问统计

联系我们

[1]	矫腾章, 胡玉新, 吕鹏, 张凯, 台宪青. 一种在轨海上多运动舰船目标检测和跟踪方法[J]. 中国科学院大学学报, 2020, 37(3): 368-378.
[2]	刘桂林, 张落成, 赵金丽. 景观破碎化区域土地覆被面向对象分类研究——以皖南山区为例[J]. 中国科学院大学学报, 2014, 31(5): 626-631.
[3]	赵理君, 唐家奎, 于新菊, 王春磊, 张成雯. 综合视觉特征度量与SVM的遥感图像检索方法[J]. 中国科学院大学学报, 2013, 30(3): 347-352.
[4]	陈刚, 王宏琦, 孙显. 基于核函数原型和自适应遗传算法的 SVM模型选择方法[J]. 中国科学院大学学报, 2012, 29(1): 62-69.