DCA FEATURE FUSION TERRAIN CLASSIFICATION BASED ONSPATIAL PYRAMID MODEL

Li Cuiming; Xu Longer; Wang Long; Wang Hua; Shen Tao

doi:10.19912/j.0254-0096.tynxb.2022-0727

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (9) : 334-339. DOI: 10.19912/j.0254-0096.tynxb.2022-0727

DCA FEATURE FUSION TERRAIN CLASSIFICATION BASED ONSPATIAL PYRAMID MODEL

Li Cuiming, Xu Longer, Wang Long, Wang Hua, Shen Tao

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Abstract

Aiming at the problem that the spatial pyramid bag of visual words model does not express clearly the terrain texture of terrain, the color information of the terrain is ignored and high feature dimension, a DCA feature fusion terrain classification algorithm based on the spatial pyramid bag of visual words model is proposed. This method optimizes the sub-region division method of the traditional spatial pyramid model, extracts the optimized SPM-BOVW features, HSV features, and LBP features of the terrain image; constructs three sets of transformation features through the DCA algorithm; and fuses the transformation features in series. The experimental results show that taking the fused features as the input of support vector machines（SVM ）classifier and optimizing through grid parameters, a high accuracy of terrain classification is finally obtained, which shows that the proposed algorithm has good robustness in the terrain classification of solar power station.

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solar power / feature extraction / support vector machines（SVM） / feature fusion / terrain classification

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Li Cuiming, Xu Longer, Wang Long, Wang Hua, Shen Tao. DCA FEATURE FUSION TERRAIN CLASSIFICATION BASED ONSPATIAL PYRAMID MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 334-339 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0727

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