Image registration and stitching algorithm of rice low-altitude remote sensing based on Harris corner self-adaptive detection

被引:0
|
作者
College of Engineering, South China Agricultural University, Engineering Research Center for Agricultural Aviation Application , Guangzhou [1 ]
510642, China
不详 [2 ]
410128, China
机构
来源
Nongye Gongcheng Xuebao | / 14卷 / 186-193期
关键词
Automation - Edge detection - Antennas - Optimization - Testing - Unmanned aerial vehicles (UAV) - Image registration - Pixels - Image enhancement - Remote sensing - Signal detection - Statistics - Aircraft detection;
D O I
10.11975/j.issn.1002-6819.2015.14.026
中图分类号
学科分类号
摘要
Automation of images registration and stitching is one of the most important key technologies to the wide use of the low-altitude remote sensing by Micro-UAVs (unmanned aerial vehicles) in rice growing. In order to overcome the limitations, i.e. the thresholds need to be artificially determined for the traditional Harris corner detection algorithm, this paper proposed a self-adaptive algorithm for Harris corner detection, which was used in image registration and stitching of the rice low-altitude remote sensing. The algorithm was improved based on the traditional Harris corner detection algorithm by using a self-adaptive threshold determination method, which calculated from the standard deviation of image pixel gray-scale value. And then the characteristics of image were described by corners, and the images were registered by using the Euclidean distance among descriptors. In order to verify the effectiveness of the algorithm and optimize the relevant parameters, a verification test was conducted based on low-altitude remote sensing images, which were gained by a multispectral camera mounted on a multi-rotor unmanned helicopter during rice tillering stage. Four indices, the repetition rate (a measure of the stability of corner detection), the recognition rate (a measure of corner recognizable description operator), the registration rate (a measure of the accuracy of image registration and stitching) and running time of algorithm (a measure of computing speed of the algorithm), were proposed to evaluate the results of registration and stitching. Sixty images were randomly divided into 3 groups for verification test. Test results showed that the average registration rate reached 98.95%, and also the average repetition rate reached 96%, which indicated that the proposed algorithm had high accuracy. The repetition rate and the difference in image registration rates among the groups were not significant (at 0.05 significance level), which indicated that the proposed algorithm was stable and reliable. And the recognition rate among the groups was significant, and it indicated that the proposed algorithm had higher distinguishability to the remote sensing images, which was conducive to the precision of the automation of images registration and stitching. Threshold value of the proposed algorithm, which is the standard deviation of the image pixel gray values after standardization, here is set to 1 and 2 for optimization test. Test results showed that the registration rate was not significant, namely there was no significant difference (at 0.05 significance level) when the threshold value was equal to 1 or 2. However, comparing the average running time of the proposed algorithm, it showed that the running time when the threshold value was 1, is 2.5 times that when the threshold value was 2. Based on comprehensive consideration of the registration rate, the running time and the efficiency, the threshold value of 2 can be set as the optimum parameter of the proposed algorithm. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved.
引用
收藏
相关论文
共 50 条
  • [41] A conflict detection algorithm for low-altitude flights based on SVM
    Han D.
    Zhang X.
    Nie Z.
    Guan X.
    Zhang, Xuejun (zhxj@buaa.edu.cn), 2018, Beijing University of Aeronautics and Astronautics (BUAA) (44): : 576 - 582
  • [42] A Self-adaptive Remote Sensing Image Enhancement Method Based on Gradient and Intensity Histogram
    Lu, Zhuanli
    Liu, Jiahang
    Chen, Tieqiao
    Kang, Chaomeng
    Yu, Kai
    AOPC 2017: OPTICAL SENSING AND IMAGING TECHNOLOGY AND APPLICATIONS, 2017, 10462
  • [43] Stable aerial image registration for people detection from a low-altitude aerial vehicle
    Iwashita, Yumi
    Takefuji, Yuki
    Kurazume, Ryo
    2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2016, : 4435 - 4439
  • [44] Accurate Strawberry Plant Detection System Based on Low-altitude Remote Sensing and Deep Learning Technologies
    Zhang, Huazhe
    Lin, Ping
    He, Jianqiang
    Chen, Yongming
    2020 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD 2020), 2020, : 1 - 5
  • [45] Stepwise local stitching ultrasound image algorithms based on adaptive iterative threshold Harris corner features
    Sun, Hongfei
    Yang, Jianhua
    Fan, Rongbo
    Xie, Kai
    Wang, Conghui
    Ni, Xinye
    MEDICINE, 2020, 99 (37) : E22189
  • [46] Algorithm of Sub-pixel Image Registration Based on Harris Corner and SIFT Descriptor
    Zhu Jianguo
    Fan Guihua
    7TH INTERNATIONAL SYMPOSIUM ON ADVANCED OPTICAL MANUFACTURING AND TESTING TECHNOLOGIES: OPTICAL TEST AND MEASUREMENT TECHNOLOGY AND EQUIPMENT, 2014, 9282
  • [47] Saliency detection based on self-adaptive multiple feature fusion for remote sensing images
    Zhang, Libao
    Liu, Yanan
    Zhang, Jue
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (22) : 8270 - 8297
  • [48] Registration algorithm for agricultural aviation remote sensing image based on point feature detection
    Lu J.
    Li W.
    Lan Y.
    He B.
    Lin J.
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2020, 36 (03): : 71 - 77
  • [49] A Deep-Learning- Based Low-Altitude Remote Sensing Algorithm for Weed Classification in Ecological Irrigation Area
    Wang, Shubo
    Han, Yu
    Chen, Jian
    Pan, Yue
    Cao, Yi
    Meng, Hao
    Zheng, Yongjun
    INTELLIGENT TECHNOLOGIES AND APPLICATIONS, INTAP 2018, 2019, 932 : 451 - 460
  • [50] A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images
    Shao, Zhenfeng
    Yang, Nan
    Xiao, Xiongwu
    Zhang, Lei
    Peng, Zhe
    REMOTE SENSING, 2016, 8 (05)