A combined algorithm of improved LBP and HSV for surface state recognition

doi:10.13474/j.cnki.11-2246.2020.0040

Abstract

Abstract: Different surface states will lead to different modes of electromagnetic wave propagation, which determines the different detection methods, detection wavelengths and detection methods of remote sensing, when the surface information is retrieved by near-surface remote sensing. Therefore, surface state recognition is the premise of remote sensing retrieval of near-surface, and recognition accuracy determines remote sensing retrieval accuracy. A combined algorithm of LBP(local binary pattern)and HSV color histogram for surface state recognition is proposed. Feature vector extraction is carried out by combining the Improved Threshold LBP algorithm and HSV color histogram, then discriminant condition is established, in the end, the results of recognition can be realized by feature matching between test samples and training samples, based on K-neighborhood algorithm. 411 images collected in the field are divided into training samples and test samples randomly. The correct rate of recognition for 206 test samples is more than 98.7%, It is proved that the algorithm is effective.

Key words: image recognition, improved LBP, HSV color feature, K-neighborhood classification, surface image

CLC Number:

P237

SUN Yishan, LI Xiaojie, ZHAO Kai. A combined algorithm of improved LBP and HSV for surface state recognition[J]. Bulletin of Surveying and Mapping, 2020, 0(2): 29-36,71.

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