Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1023-1028, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1023-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1023-1028, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1023-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

UAV-BASED CROPS CLASSIFICATION WITH JOINT FEATURES FROM ORTHOIMAGE AND DSM DATA

B. Liu, Y. Shi, Y. Duan, and W. Wu B. Liu et al.
  • Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, China /Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Keywords: UAV, crop classification, SVM, DOM, DSMs, Texture

Abstract. Accurate crops classification remains a challenging task due to the same crop with different spectra and different crops with same spectrum phenomenon. Recently, UAV-based remote sensing approach gains popularity not only for its high spatial and temporal resolution, but also for its ability to obtain spectraand spatial data at the same time. This paper focus on how to take full advantages of spatial and spectrum features to improve crops classification accuracy, based on an UAV platform equipped with a general digital camera. Texture and spatial features extracted from the RGB orthoimage and the digital surface model of the monitoring area are analysed and integrated within a SVM classification framework. Extensive experiences results indicate that the overall classification accuracy is drastically improved from 72.9 % to 94.5 % when the spatial features are combined together, which verified the feasibility and effectiveness of the proposed method.