Object-based multispectral image fusion method using deep learning

被引:2
作者
Jang, Hyunsung [1 ,2 ]
Ha, Namkoo [1 ]
Yeon, Yoonmo [1 ]
Kwon, Kuyong [1 ]
Gil, Sungho [1 ]
Lee, Seungha [1 ]
Park, Sungsoon [1 ]
Jung, Hyungjoo [2 ]
Sohn, Kwanghoon [2 ]
机构
[1] LIG Nex1 Co Ltd, Yongin 16911, South Korea
[2] Yonsei Univ, Sch Elect & Elect Engn, Seoul 03722, South Korea
来源
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DEFENSE APPLICATIONS | 2019年 / 11169卷
关键词
Multispectral; Image fusion; Deep learning; Unsupervised learning; Computer vision;
D O I
10.1117/12.2532718
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The goal of multispectral image fusion is to integrate complementary information from multispectral sensors to enhance human visual perception and object detection. Additionally, there are also cases when only the object needs to be emphasized with minimal background interference. This paper presents an object-based fusion method using deep learning to accomplish this objective. The proposed method uses information regarding the region of an object to perform fusion on the object. As we cannot provide labels for fusion results at the learning stage, we propose an unsupervised learning method. The proposed method simultaneously provides appropriate image information from the background and target for surveillance and reconnaissance.
引用
收藏
页数:6
相关论文
共 11 条
[1]  
Connah D, 2014, LECT NOTES COMPUT SC, V8693, P65, DOI 10.1007/978-3-319-10602-1_5
[2]   POP image fusion - derivative domain image fusion without reintegration [J].
Finlayson, Graham D. ;
Hayes, Alex E. .
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, :334-342
[3]   Identity Mappings in Deep Residual Networks [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
COMPUTER VISION - ECCV 2016, PT IV, 2016, 9908 :630-645
[4]   Gradient-based learning applied to document recognition [J].
Lecun, Y ;
Bottou, L ;
Bengio, Y ;
Haffner, P .
PROCEEDINGS OF THE IEEE, 1998, 86 (11) :2278-2324
[5]   Feature Pyramid Networks for Object Detection [J].
Lin, Tsung-Yi ;
Dollar, Piotr ;
Girshick, Ross ;
He, Kaiming ;
Hariharan, Bharath ;
Belongie, Serge .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :936-944
[6]  
Long J, 2015, PROC CVPR IEEE, P3431, DOI 10.1109/CVPR.2015.7298965
[7]   Large Kernel Matters - Improve Semantic Segmentation by Global Convolutional Network [J].
Peng, Chao ;
Zhang, Xiangyu ;
Yu, Gang ;
Luo, Guiming ;
Sun, Jian .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :1743-1751
[8]   U-Net: Convolutional Networks for Biomedical Image Segmentation [J].
Ronneberger, Olaf ;
Fischer, Philipp ;
Brox, Thomas .
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III, 2015, 9351 :234-241
[9]   Multispectral image visualization through first-order fusion [J].
Socolinsky, DA ;
Wolff, LB .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2002, 11 (08) :923-931
[10]  
Zhang X, 2013, ARXIV PREPRINT ARXIV