TY - GEN
T1 - Research on the detection of the UAV remote sensing chili images based on superpixel segmentation and SVM
AU - Zhang, Di
AU - Pan, Feng
AU - Xing, Boyang
AU - An, Qichao
AU - Wang, Rui
AU - Qi, DIao
N1 - Publisher Copyright:
© 2019 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2019/7
Y1 - 2019/7
N2 - The collection of agricultural field sample sets needs generally to be carried out according to the crop growth cycle, and it is not easy to obtain large amounts of data. For specific crop detection problems, it often takes time and effort to perform data labeling. Aiming at the problem of detecting the unmanned aerial remote sensing chili images, this paper proposes a recognition framework of the chili images, which mainly contains three processes. Firstly, the classifier is designed, and the remote sensing image is automatically sliced to make the sample set. Then, the SVM classifier is trained. Secondly, it is the detection process. The input image is segmented into superpixel to generate candidate region, and then the regions are classified using a classifier to complete the detection process. Finally, the results of detection are fine-tuned by the method of template matching, and the sample set is updated with a part of the detection result in chili image patches.
AB - The collection of agricultural field sample sets needs generally to be carried out according to the crop growth cycle, and it is not easy to obtain large amounts of data. For specific crop detection problems, it often takes time and effort to perform data labeling. Aiming at the problem of detecting the unmanned aerial remote sensing chili images, this paper proposes a recognition framework of the chili images, which mainly contains three processes. Firstly, the classifier is designed, and the remote sensing image is automatically sliced to make the sample set. Then, the SVM classifier is trained. Secondly, it is the detection process. The input image is segmented into superpixel to generate candidate region, and then the regions are classified using a classifier to complete the detection process. Finally, the results of detection are fine-tuned by the method of template matching, and the sample set is updated with a part of the detection result in chili image patches.
KW - Candidate Region Generation
KW - SVM
KW - Superpixel Segmentation
KW - Template Matching
KW - UAV Remote Sensing Images
UR - https://www.scopus.com/pages/publications/85074389429
U2 - 10.23919/ChiCC.2019.8865686
DO - 10.23919/ChiCC.2019.8865686
M3 - Conference contribution
AN - SCOPUS:85074389429
T3 - Chinese Control Conference, CCC
SP - 7828
EP - 7834
BT - Proceedings of the 38th Chinese Control Conference, CCC 2019
A2 - Fu, Minyue
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 38th Chinese Control Conference, CCC 2019
Y2 - 27 July 2019 through 30 July 2019
ER -