Robust Hyperspectral Anomaly Detection via Unsupervised Multiscale Feature Fusion

In hyperspectral image (HSI) anomaly detection (AD) methods, detecting small targets or anomalies remains challenging. This difficulty arises because targets or anomalies may vary significantly in size, shape, and texture, causing them to be obscured by larger-scale background features. To address the above issue, this paper proposes an unsupervised multi-scale feature fusion network (UMF2Net) for HSI-AD. Firstly, central difference convolution analyzes the image at multiple scales to capture fine-to-coarse details and structural information. Additionally, three-dimensional (3D) convolution is employed to generate feature weights for the multi-scale features, assigning different weights to features with different contributions so that the model dynamically emphasizes features that have a greater impact on the AD results. Finally, by using the two proposed multi-scale feature fusion modules, the model effectively integrates features at different scales, thereby enhancing its ability to detect anomalies of varying sizes. Compared with several classical HSI-AD algorithms on real hyperspectral datasets from four scenarios, UMF2Net achieved competitive detection results, verifying the effectiveness of our algorithm.