Research on FNEA Object-oriented Classification Based on Multi-scale Partition Parameters

doi:10.13474/j.cnki.11-2246.2018.0073

Abstract

Abstract:

In this paper,the fractal net evolution approach (FNEA) is introduced,and then the effects of four segmentation methods,chessboard segmentation,quadtree-based segmentation,multiresolution segmentation and spectral difference segmentation are compared.In addition,the eCognition Developer 8.7 analysis software is used to perform multi-scale segmentation preprocessing of the image,starting from 5 and increasing in 5 increments.A total of 12 sets of parameters are selected for segmentation experiments.The object-oriented CART classifier is used to classify the segmented image.Compared with the overall accuracy and Kappa coefficient,it can be seen that the classification accuracy of small-scale segmentation parameters is better than that of large-scale segmentation parameters.When the segmentation parameter scale is 10,the small-scale segmentation parameters can be better than the large-classification accuracy to achieve the best level.

Key words: fractal net evolution approach, segmentation method, multi-scale segmentation, parameter selection, CART classifier

CLC Number:

P237

SUN Kun, LU Tieding. Research on FNEA Object-oriented Classification Based on Multi-scale Partition Parameters[J]. 测绘通报, 2018, 0(3): 43-48.

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