Target fast matching recognition of on-board system based on Jilin-1 satellite image

Wei XU; Yan-tong CHEN; Yong-jie PIAO; Shao-ju WANG

doi:10.3788/OPE.20172501.0255

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Target fast matching recognition of on-board system based on Jilin-1 satellite image

Information Sciences | 更新时间：2020-07-07

- Target fast matching recognition of on-board system based on Jilin-1 satellite image
- Optics and Precision Engineering Vol. 25, Issue 1, Pages: 255-262(2017)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172501.0255
  CLC： TP753
- Received：08 April 2016，
  
  Accepted：01 June 2016，
  
  Published：25 January 2017
- 稿件说明：
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Wei XU, Yan-tong CHEN, Yong-jie PIAO, et al. Target fast matching recognition of on-board system based on Jilin-1 satellite image[J]. Optics and precision engineering, 2017, 25(1): 255-262.
DOI：

Wei XU, Yan-tong CHEN, Yong-jie PIAO, et al. Target fast matching recognition of on-board system based on Jilin-1 satellite image[J]. Optics and precision engineering, 2017, 25(1): 255-262. DOI： 10.3788/OPE.20172501.0255.

摘要

针对传统遥感图像地面目标识别系统图像获取周期长，信息实时性差等问题，设计星载目标快速识别系统，用于卫星在轨快速识别，提出改进的基于快速视网膜关键点（FREAK）的特征匹配识别算法，解决遥感图像数据量大、背景复杂的问题。介绍了星载目标快速识别系统的工作原理，提出简化的FREAK特征提取模型，将原有算法的七层模型减少为四层，用于快速提取出遥感图像中目标特征；利用二进制量化空间将高维特征数据量化为二维数据，提高算法的准确度；最后通过匹配，快速识别出遥感目标。实验结果表明，识别算法的准确度平均提高2.3%，识别用时缩短约27.8%，满足遥感卫星在轨目标快速识别的要求。

Abstract

Aiming at problems such as long cycle and insufficient real time information in traditional remote sensing ground target image recognition system

an on-board target fast matching recognition platform is designed for fast on-orbit satellite recognition

and an improved feature matching recognition algorithm based on fast retinal key points (FREAK) is proposed to solve the problems of complex backgrounds and large amount of data in remote sensing image

First

we introduce the principle of on-board target recognition system and propose the simplified FREAK feature extraction model

and then we reduce the model of original algorithm from seven floors to four to quickly extract target features in remote sensing image. And then the high-dimensional feature data is quantified into two-dimensional data using binary quantization space

thus improving the accuracy of the algorithm; finally

the remote targets are recognized quickly by matching. The experimental results show that the matching accuracy can be increased by 2.3%

and matching time can be reduced by 27.8%. It can meet the requirements of quick identification of remote sensing satellite on-orbit targets.

关键词

Keywords

references

徐伟,朴永杰. 从Pleiades剖析新一代高性能小卫星技术发展[J]. 中国光学,2013,6(1):9-19.

XU W, PIAO Y J.Analysis of new generation high-performance small satellite technology based on the Pleiades[J]. Chinese Optics, 2013, 6(1):9-19.(in Chinese)

陈彦彤,王绍举. 高分辨遥感图像目标识别技术综述[J]. 中国光学,2014,7(37):17-23.

CHEN Y T, WANG SH J. Review of target recognition technology for high resolution remote sensing image[J].Chinese Optics, 2014, 7(37):17-23.(in Chinese)

BRIESS K, JAHN H. Fire recognition potential of the Bi-spectral Infrared Detection(BID) satellite[J].International Journal of Remote Sensing, 2003, 24(4):865-872.

TILO B, DIMA D. Correspondence, matching and recognition[J].IEEE Trans. on International Journal of Computer Vision, 2015, 113(3):161-162.

赵立荣,朱玮,曹永刚,等. 改进的加速鲁棒特征算法在特征匹配中的应用[J]. 光学精密工程,2013,21(12):3263-3271.

ZHAO L R, ZHU W, CAO Y G, et al.. Application of improved SURF algorithm to feature matching[J].Opt. Precision Eng., 2013, 21(12):3263-3271.(in Chinese)

KORMAN S, REICHMAN D.FAsT-Match:fast affine template matching[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013:2331-2338.

邵振峰,陈敏. 尺度旋转以及亮度稳健的高分辨率影像直线特征匹配[J]. 光学精密工程,2013,21(3):790-798.

SHAO ZH F, CHEN M. Line-based matching for high-resolution images with robustness for scale, rotation and illumination[J].Opt. Precision Eng., 2013, 21(3):790-798.(in Chinese)

ALAHI A, POLYTECH E FREAK:Fast retina keypoints[C]. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012:510-517.

LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.

BAY H, ESS A, TUYTELAARS T. SURF:Speeded up robust features[J]. Computer Vision and Image Understanding, 2008, 110(3):346-359.

LEE H, CHANG J. Signature-based hybrid spill-tree for indexing high-dimensional data[C]. inProceedings of the 9th International Conference on Computer and Information Technology, 2009:287-292.

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