Aerial Scene Classification Based on Nonlinear Scale Space

doi:10.16183/j.cnki.jsjtu.2017.10.012

Abstract

Abstract: In aerial scene classification, scale invariant feature transform (SIFT) uses linear Gaussian decomposition to extract feature points. The algorithm has many problems, such as fuzzy boundary and loss of detail. Besides, the SIFT cannot describe the color information. Combined with bagofvisualwords (BoVW) model, CKAZE (colorKAZE) descriptor which fuses color feature in nonlinear scale space is proposed to solve these problems. KAZE is used to detect the characteristic information by constructing nonlinear scale space. Color quantization matrix is calculated by noninterval quantization in the HSV (hue, saturation, value) space, and the CKAZE feature descriptor is generated by the quantization matrix. Finally, highlevel semantic features and spatial layout information are extracted and fused. Experimental results show that the average classification accuracy of the proposed algorithm, compared to the classification algorithm based on SIFT, is improved by about 8%. The proposed algorithm improves the feature description ability of KAZE, and breaks the limitation of the SIFT classification algorithm. Besides, for the unmanned aerial vehicle (UAV) scene image, the accuracy can be greatly improved.

Key words: unmanned vehicle aerial image; scene classification; colorKAZE; nonlinear scale space

CLC Number:

TP 391.4

CHEN Suting，WANG Zhuo，WANG Qi. Aerial Scene Classification Based on Nonlinear Scale Space[J]. Journal of Shanghai Jiao Tong University, 2017, 51(10): 1228-1234.

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