Deep multi-scale convolutional neural network for hyperspectral image classification

Feng-zhe Zhang; Xia Yang

doi:10.1117/12.2304916

10 April 2018 Deep multi-scale convolutional neural network for hyperspectral image classification

Feng-zhe Zhang, Xia Yang

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152Z (2018) https://doi.org/10.1117/12.2304916
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

Citation Download Citation

Feng-zhe Zhang and Xia Yang "Deep multi-scale convolutional neural network for hyperspectral image classification", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152Z (10 April 2018); https://doi.org/10.1117/12.2304916

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