UAV Image Detecting of Single Building's Angular Points Method Based on SVM

doi:10.13474/j.cnki.11-2246.2017.0502

Abstract

Abstract: In view of the present situation of single building's angular points detection in UAV images, this paper proposes a method that based on support vector machine (SVM) to detect the corner of the building.Firstly,the UAV image with four bands to complete multi-scale segmentation,calculates the NDVI of this image,to eliminate the effect of vegetation by the spectral differences between vegetation and non-vegetation areas;Secondly, using object-oriented classification to extract "building block" from the image,and the edge detection for the "building block" completed by Harris,then it comes into being edge point set of building and extracts some points as samples randomly. The edge sample points are mapped to high-dimensional feature spaces by Gauss RBF and construct the feature vector,the SVM classification model is trained by edge point set.Finally,the correct building corner is detected by the SVM classification model from the rough edge points, and the corner of the single building is extracted.

Key words: support vector machine, Harris algorithm, building, corner detection

CLC Number:

P237

LI Lingzhi, LI Baishou, SHEN Yuzhen, XU Rui. UAV Image Detecting of Single Building's Angular Points Method Based on SVM[J]. 测绘通报, 2017, 0(10): 52-57,78.

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