Hyperspectral image classification via a random patches network

Due to the remarkable achievements obtained by deep learning methods in the fields of computer vision, an increasing number of researches have been made to apply these powerful tools into hyperspectral image (HSI) classification. So far, most of these methods utilize a pre-training stage followed by a fine-tuning stage to extract deep features, which is not only tremendously time-consuming but also depends largely on a great deal of training data. In this study, we propose an efficient deep learning based method, namely, Random Patches Network (RPNet) for HSI classification, which directly regards the random patches taken from the image as the convolution kernels without any training. By combining both shallow and deep convolutional features, RPNet has the advantage of multi-scale, which possesses a better adaption for HSI classification, where different objects tend to have different scales. In the experiments, the proposed method and its two variants RandomNet and RPNet-single are tested on three benchmark hyperspectral data sets. The experimental results demonstrate the RPNet can yield a competitive performance compared with existing methods.