Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1415-1420, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1415-2018
© 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, 1415-1420, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1415-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

METHOD OF GRASSLAND INFORMATION EXTRACTION BASED ON MULTI-LEVEL SEGMENTATION AND CART MODEL

Y. Qiao1, T. Chen1, J. He1, Q. Wen1, F. Liu1, and Z. Wang2 Y. Qiao et al.
  • 1Twenty First Century Aerospace Technology Co., Ltd., Beijing, China
  • 2Beijing Engineering Research Center of Small Satellite Remote Sensing Information, Beijing, China

Keywords: Grassland Extraction, Multi-level Segmentation, Feature Selection, CART Model, Xilinhaote City

Abstract. It is difficult to extract grassland accurately by traditional classification methods, such as supervised method based on pixels or objects. This paper proposed a new method combing the multi-level segmentation with CART (classification and regression tree) model. The multi-level segmentation which combined the multi-resolution segmentation and the spectral difference segmentation could avoid the over and insufficient segmentation seen in the single segmentation mode. The CART model was established based on the spectral characteristics and texture feature which were excavated from training sample data. Xilinhaote City in Inner Mongolia Autonomous Region was chosen as the typical study area and the proposed method was verified by using visual interpretation results as approximate truth value. Meanwhile, the comparison with the nearest neighbor supervised classification method was obtained. The experimental results showed that the total precision of classification and the Kappa coefficient of the proposed method was 95 % and 0.9, respectively. However, the total precision of classification and the Kappa coefficient of the nearest neighbor supervised classification method was 80 % and 0.56, respectively. The result suggested that the accuracy of classification proposed in this paper was higher than the nearest neighbor supervised classification method. The experiment certificated that the proposed method was an effective extraction method of grassland information, which could enhance the boundary of grassland classification and avoid the restriction of grassland distribution scale. This method was also applicable to the extraction of grassland information in other regions with complicated spatial features, which could avoid the interference of woodland, arable land and water body effectively.