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One dimensional convolutional neural networks and local binary patterns for hyperspectral image classification | IEEE Conference Publication | IEEE Xplore

One dimensional convolutional neural networks and local binary patterns for hyperspectral image classification


Abstract:

This paper proposes a network classification framework based on multiresolution local binary pattern techniques and convolutional neuronal network models. Local binary pa...Show More

Abstract:

This paper proposes a network classification framework based on multiresolution local binary pattern techniques and convolutional neuronal network models. Local binary pattern is used as a descriptor in order to evaluate the data in the spatial domain of the hyperspectral data. The convolutional neural networks offer powerful feature extraction capabilities over the spectral information defining the high-resolution data sets. Our proposed model is based on one dimensional convolutional network (1D-CNN) applied over the histograms obtained from the rotational invariant local binary pattern technique. With respect to other concurrent methods applied on textured hyperspectral data bases, the novel proposed method has similar results with other LBP-ID-CNN classification networks, but has smaller number of parameters and reduced time for training and testing. The experimental results demonstrate that the proposed method can improve the accuracy on Indian Pines and Salinas data sets even with a limited number of training samples.
Date of Conference: 21-23 May 2020
Date Added to IEEE Xplore: 01 July 2020
ISBN Information:
Conference Location: Cluj-Napoca, Romania

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