A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 4607 - 4620
• Main Authors: Huang, Wei, Huang, Yao, Wu, Zebin, Yin, Junru, Chen, Qiqiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classification; Convolution; Correlation; Deep learning; Feature extraction; hyperspectral image (HSI) classification; Hyperspectral imaging; Image classification; Kernel; Kernel functions; Licenses; local binary pattern (LBP); Machine learning; Methods; multikernel mode; Multilayers; Neural networks; Principal component analysis; Radial basis function; random patches; Support vector machines; Training
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3076198

With the development of deep learning technology, more and more scholars have applied it to hyperspectral image (HSI) classification to improve classification accuracy. However, these deep-learning methods not only take a lot of time in the pre-training phase, but also have relatively limited classification performance when there are fewer labeled samples. In order to improve classification performance while reducing costs, this article proposes a multikernel method based on a local binary pattern and random patches (LBPRP-MK), which integrates a local binary pattern (LBP) and deep learning into a multiple-kernel framework. First, we use LBP and hierarchical convolutional neural networks to extract local textural features and multilayer convolutional features, respectively. The convolution kernel for the convolution operation is obtained from the original image using a random strategy without training. Then, we input local textural features, multilayer convolutional features, and spectral features obtained from the original image into the radial basis function to obtain three kernel functions. Finally, the three kernel functions are merged into a multikernel function according to their optimal weights under the composite kernel strategy. This multikernel function is used as the input for the support vector machine to obtain the classification result map. Experiments show that compared with other HSI classification methods, the proposed method achieves better classification performance on three HSI datasets.