Convolutional Neural Network for Natural Color Visualization of Hyperspectral Images

In this paper, a novel deep learning based visualization method is proposed for natural color visualization of hyperspectral images, which consists of the following steps. First, the spectral bands of the hyperspectral image are divided into two groups, i.e., the red, green, and blue (RGB) bands and the remaining bands. Then, a pretrained convolutional neural network (CNN) model, i.e., VGG-19, is explored to fuse the remaining bands so as to obtain a fused band with rich details. Next, the intensity-hue-saturation (IHS) transform is performed on the averaged red, green, and blue bands to obtain three different components, i.e., intensity (I), hue (H), saturation (S). Finally, the fused band is utilized to replace the intensity component followed by an inverse IHS transform. Experiments performed on two hyperspectral data sets demonstrate that the proposed method cannot only obtain a natural color resulting image, but also well preserving image details with respect to several state-of-the-art methods.