Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 199-206, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-199-2018
© 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, 199-206, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-199-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

OBJECT-BASED RANDOM FOREST CLASSIFICATION OF LAND COVER FROM REMOTELY SENSED IMAGERY FOR INDUSTRIAL AND MINING RECLAMATION

Y. Chen1,2, M. Luo1, L. Xu2, X. Zhou1, J. Ren1, and J. Zhou1 Y. Chen et al.
  • 1Land Consolidation and Rehabilitation Center, Ministry of Land and Resource, China, People's Republic of
  • 2China University of Geosciences (Beijing), School of Land Science and Technology, China, People's Republic of

Keywords: Reclamation Area, Classification of Land Use, Random Forest, Grid-search, Object-based, Multi-resolution Segmentation, Multi-feature Variables

Abstract. The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.