Multi-dimensional deep dense residual networks and multiple kernel learning for hyperspectral image classification

Abstract

To address the issues of inadequate feature expression capacity and poor adaptability of feature fusion in traditional hyperspectral image classification methods, a new approach to classification utilizing a multi-dimensional deep dense residual network and multiple kernel learning is suggested. First, we construct a deep dense residual structure to obtain the spectral, spatial and spectral-spatial features of the image, respectively. Then, the multiple kernel learning is employed to map the three extracted features into the high-dimensional space to achieve the adaptive fusion between features. Finally, multiple kernel support vector machine is adopted to accurately classify the multiple kernel fused features. By randomly selecting 10%, 1% and 1% of samples in each ground object category as training samples, our proposed method demonstrated an enhanced overall classification accuracy of 99.58%, 99.52% and 99.66% according to the experimental results conducted on three typical hyperspectral images. Comparing with some multiple kernel learning based methods, our method can obtain higher classification accuracy. So our method has better recognition ability for hyperspectral image features, and has strong adaptability and robustness.