The hyperspectral feature extraction technique is one of the most popular topics in the remote sensing community. However, most hyperspectral feature extraction methods are based on region-based local information descriptors while neglecting the correlation and dependencies of different homogeneous regions. To alleviate this issue, this paper proposes a multi-view structural feature extraction method to furnish a complete characterization for spectral–spatial structures of different objects, which mainly is made up of the following key steps. First, the spectral number of the original image is reduced with the minimum noise fraction (MNF) method, and a relative total variation is exploited to extract the local structural feature from the dimension reduced data. Then, with the help of a superpixel segmentation technique, the nonlocal structural features from intra-view and inter-view are constructed by considering the intra- and inter-similarities of superpixels. Finally, the local and nonlocal structural features are merged together to form the final image features for classification. Experiments on several real hyperspectral datasets indicate that the proposed method outperforms other state-of-the-art classification methods in terms of visual performance and objective results, especially when the number of training set is limited.

With the rapid development of artificial intelligence technology, image editing technology has evolved from relying on software such as Photoshop and GIMP for manual modification to using artificial intelligence technology to achieve intelligent and automated tampering of images. Editing, falsifying, and disseminating digital images have become simple and easy, leading to a crisis of confidence in digital images and reducing their reliability as judicial evidence. Therefore, how to identify falsified images, improve their trustworthiness, and avoid judicial injustice has become a problem that must be overcome in the information age. In this paper, we propose a target detection and adaptive feature analysis in image forensics based on a dual-flow layer CNN model, which can effectively perform image forensics. The results show that our algorithm has a clear theoretical basis, a small operational complexity, and a high detection accuracy.