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- Title
基于无人机多光谱影像的地物识别.
- Authors
魏青; 张宝忠; 魏征
- Abstract
【Objective】 In view of the lack of timeliness of farmland information acquisition and the difficulty of grasping basic farmland information in time, in this project, the UAV multi-spectral images acquired in May and June 2018 were used to study the extraction of some farmland types in Daxing experimental base in Beijing. 【Method】Firstly, the species of interest were identified, and the temporal and spectral characteristics of the image were analyzed. Then, the normalized vegetation index NDVI, normalized green-blue difference index NGBDI, modified ratio vegetation index MSR and red-band reflectance were determined as the optimal classification features, and the image was segmented by threshold based on spectral variables. The decision tree classification method based on visual interpretation was used to realize the classification of land features and extract the planting area. The method was validated by selecting the ground survey data based on visual interpretation. 【Result】 The results showed that the decision tree classification method based on temporal and spectral characteristics had good effect and the method was applicable to extracting wheat, fruit trees and big shed with errors of 10.68%, 6.06% and 16.48%, respectively. Besides, the area extraction error was within 17%, so we can safely say that UAV multi-spectral remote sensing image has certain applicability for ground object recognition. 【Conclusion】 The advantages of UAV in low cost and high efficiency provide reference for timely access to farmland information.
- Publication
Xinjiang Agricultural Sciences, 2020, Vol 57, Issue 5, p932
- ISSN
1001-4330
- Publication type
Article
- DOI
10.6048/j.issn.1001-4330.2020.05.018