|
|
|
|
|
|
| Research on Image Data Matching Method Based on Infrared Spectrum Technology of UAV |
| TAN Xiang1, 2, MAO Hai-ying2, ZHI Xiao-dong3, HU Xing-bang1, MA Ai-nai1, YAN Lei1* |
1. Beijing Key Lab of Spatial Information Integration &3S Application, Peking University, Beijing 100871, China
2. Specialized Forces College of the Chinese Armed Police Force, Beijing 102202, China
3. Feima Robotics Co., Ltd., Shenzhen 518000, China |
|
|
|
|
Abstract UAV loading infrared/near-infrared spectroscopy on regional image acquisition load has become an important field of remote sensing technology, through classifying the position information of the portable image, and getting the vegetation cover, temperature index and a series of factors. In this paper, we used FREE BIRD low altitude unmanned aerial vehicle (UAV) to mount Tetracam- infrared camera (3 million 100 thousand pixels) to get the image of a river in Xinjiang, Manasi. In order to get more accurate vegetation temperature and other factors, we needed UAV infrared/near infrared image registration, through the optimization of SIFT, detection of outliers and RANSAC parameters, to obtain reliable matching results. After the matching algorithm of the image ,the original image of the error ratio were below 60%, which was one of the innovations of this paper to meet the needs of the application. After registering the images, the images were spliced, and the infrared images were spliced according to the degree of overlap of the course of not less than 60%, while the probability of the adjacent overlap was not less than 50%. At the same time, this paper compared the SIFT and SUFT two kinds of algorithms, using FLIR sensor SIFT algorithm and improved optimization to obtain 1 600 thermal infrared image matching and image inversion of ground by utilization of synchronous measurement data. We used ENVI software to carry out the inversion of vegetation coverage temperature inversion and inversion vegetation map to get the single image and the infrared image of the study area. The algorithm model is more optimized through the comparison of the two algorithms, while the model of regression analysis and test of accuracy, the correlation coefficient R2 of the model is 0.767, and accuracy is 81.51% with higher model precision. This model provides theoretical and practical basis for the registration and extraction of inversion of UAV infrared image.
|
|
Received: 2017-03-23
Accepted: 2017-09-10
|
|
|
|
Corresponding Authors:
YAN Lei
E-mail: lyan@pku.edu.cn
|
|
[1] Tuytelaars T, Mikolajczyk K. Computer, Graphics and Vision, 2008，3(3): 177.
[2] Calonder M, Lepetit V, Strecha C, et al. Brief: Binary Robust Independent Elementary Features. Computer Vision—ECCV 2010. Springer Berlin Heidelberg, 2010. 778.
[3] Rublee E, Rabaud V, Konolige K, et al. ORB: an Efficient Alternative to SIFT or SURF. Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011. 2564.
[4] Matas J, Chum O, Urban M, et al. Image and Vision Computing, 2004, 22(10): 761.
[5] Morel J M, Yu G. SIAM Journal on Imaging Sciences, 2009, 2(2): 438.
[6] Alahi A, Ortiz R, Vandergheynst P. Freak: Fast retina Keypoint. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012. 510.
[7] Yi Z, Zhiguo C, Yang X. Electronics Letters, 2008, 44(2): 107.
[8] Aguilera C, Barrera F, Lumbreras F, et al. Sensors, 2012, 12(9): 12661.
[9] Meierhold N, Spehr M, Schilling A, et al. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 2010, 38: 446.
[10] Sima A, Buckley S J, Kurz T H, et al. Photogrammetrie-Fernerkundung-Geoinformation, 2012, 2012(4): 443.
[11] Wang W, Cao T, Liu S. J. of the Indian Society of Remote Sensing, 2015, 20(1): 71.
[12] Herbert Bay, Tinne Tuvtellars，Luc Van Gool. SURF: Speeded Up Robust Features. European Conference on Computer Vision，2006. 404.
[13] May M, Turner M J. Scale Invariant Feature Transform: A Graphical Parameter Analysis. In Proceedings of the BMVC 2010 UK. Postgraduate Workshop, Aberystwyth, UK, 31 August-3 September, 2010. 1.
[14] Bendig J, Bolten A, Bennertz S, et al. Remote Sensing, 2014, 6(11): 10395. |
| [1] |
CHENG Jia-wei1, 2，LIU Xin-xing1, 2*，ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
| [2] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
| [3] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
| [4] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [5] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
| [6] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [7] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
| [8] |
ZHU Wen-jing1, 2，FENG Zhan-kang1, 2，DAI Shi-yuan1, 2，ZHANG Ping-ping3，JI Wen4，WANG Ai-chen1, 2，WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
| [9] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [10] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [11] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
| [12] |
SUN Wei-ji1, LIU Lang1, 2*, HOU Dong-zhuang3, QIU Hua-fu1, 2, TU Bing-bing4, XIN Jie1. Experimental Study on Physicochemical Properties and Hydration Activity of Modified Magnesium Slag[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3877-3884. |
| [13] |
LI Xiao-dian1, TANG Nian1, ZHANG Man-jun1, SUN Dong-wei1, HE Shu-kai2, WANG Xian-zhong2, 3, ZENG Xiao-zhe2*, WANG Xing-hui2, LIU Xi-ya2. Infrared Spectral Characteristics and Mixing Ratio Detection Method of a New Environmentally Friendly Insulating Gas C5-PFK[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3794-3801. |
| [14] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [15] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
|
|
|
|
