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Boosting Tree Model with Gabor and LPQ Feature Fusion of HSI Ground Object Recognition |
Yanan Jiang1(),Chunlei Zhang2(),Xin Zhang3,Quanwei Xu1,Shutao Zhang1,Rui Zhou1 |
1.School of Science,China University of Geosciences(Beijing),Beijing 100083,China 2.Beijing Zhongdirunde Petroleum Technology Co. Ltd. ,Beijing 100083,China 3.School of Statistics,Beijing Normal University,Beijing 100875,China |
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Abstract To fully fuse the feature information in the spatial and frequency domains of hyperspectral image (HIS), a spatial-spectrum fusion HSI ground object recognition model that integrates multiscale features of Gabor and LPQ (Ms_GLPQ) is proposed. Firstly, the Gabor filter bank is used in the spatial domain to extract the multiscale and multidirectional spatial neighborhood information of various ground objects in HSI to describe the spatial structure of its edge and texture. Secondly, the Local Phase Quantization (LPQ) operator is utilized in the frequency domain to extract the multiscale frequency domain texture features, and the phase invariant feature description of HSI is obtained. Then the Principal Component Analysis (PCA) algorithm is used to reduce the dimensionality for the problem of feature redundancy, and the features in the spatial and frequency domains are fused to obtain the feature vector that fully describes the HSI information. Finally, the classifier based on Boosting tree (XGBoost, CatBoost, etc.) are utilized for recognition. Experiments on Indian Pines, Salinas, and tea farm datasets acquire accuracy rates of 85.88%, 94.42%, and 92.61%, respectively. The experimental results show that the Ms_GLPQ model can extract effective features in HSI and obtain more discriminative multi-featured region descriptors than traditional methods, and it performs better by using boosted tree model for ground object recognition and achieves higher accuracy than other classifiers.
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Received: 23 September 2020
Published: 17 June 2022
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Corresponding Authors:
Chunlei Zhang
E-mail: 2463613347@qq.com;676935005@qq.com
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