Abstract
It is of great interest in spectral-spatial features classification for High spectral images (HSI) with high spatial resolution. This paper presents a new Spectral-spatial method for improving accuracy of hyperspectral image classification. Specifically, a new texture feature extraction algorithm based on traditional LBP method is proposed directly. Texture profiles is obtained by the proposed method. A composite kernel framework is employed to join spatial and spectral features. The classifiers adopted in this work is the multinomial logistic regression. In order to illustrate the good performance of the proposed framework, the two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the proposed framework can enhance the classification accuracy than some traditional alternatives.
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Acknowledgments
This work has been supported by the National Science foundations of China (Grant Nos. 41301382, 61401439, 41604113, 41711530128) and foundation of key lab of spectral imaging, Xi’an Institute of Optics and Precision Mechanics of CAS.
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Wang, C., Wang, H., Ren, J., Zhang, Y., Wen, J., Zhao, J. (2018). Texture Profiles and Composite Kernel Frame for Hyperspectral Image Classification. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_31
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