EXAMINATION OF A UAV IMAGE CLASSIFICATION METHOD BY USING MACHINE LEARNING AND WAVELET TRANSFORM

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  • ウェーブレット変換と機械学習を用いたUAV河川空撮画像の地被分類手法の検討

Abstract

<p> This paper examined a machine learning technique with the wavelet transform for classifying land cover conditions in UAV (Unmanned Aerial Vehicle) images of a riverine landscape. The UAV images were taken in a river course of Kurobe River, Japan. The UAV image analyzed was composed of a RGB, NDVI (Normalized Difference Vegetation Index), and a DSM (Digital Surface Model) of the river geomorphology made from a SfM (Structure from Motion) image processing of the UAV images. In a pro-processing of the machine learning, the DSM was decomposed into the low/high wavenumber components by a wavelet transform, and further its edges were extracted for effectively utilizing the hight difference information in DSM. The result of the machine learning showed that the F-measure had high enough above 0.91 in the dataset including all characteristic values from RGB, DSM, and NDVI into the machine learning algorithm.</p>

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