Salient object detection on hyperspectral images in wireless network using CNN and saliency optimization

Salient object detection on hyperspectral images has made some progress in recent years, benefiting from the development of wireless network and hyperspectral imaging technology. However, most object detection methods on hyperspectral images focus more on the spectrum and do not fully mine the spatial information, especially high-level spatial–spectral information. In this paper, we propose a salient object detection model on hyperspectral images in wireless network by applying saliency optimization to convolutional neural network (CNN) features. In the model, we firstly use CNN with two channels to extract spatial and spectral features of the same dimension respectively and conduct feature fusion at the end. Then, we generate the final saliency maps by optimizing the saliency values of the foreground and background cues, computing from the CNN features. The experimental results confirm that the proposed method is effective and has better performance on hyperspectral images.