Hyperspectral imaging method improves detection of unusual material groups
Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping
Computer Vision and Pattern Recognition
Summary
Detecting unusual materials or objects in images that capture many colors beyond the normal visible range is important but challenging. The authors developed a new technique that breaks down complex image data into simpler parts while automatically grouping nearby unusual pixels, which helps spot anomalies more accurately. Their method looks at both color and space information together to better detect these unusual groups. They also introduced a faster way to solve the necessary calculations. Tests on real data showed their approach finds anomalies better and faster than existing methods.
hyperspectral imaginganomaly detectiontensor factorizationlow-rank modelinggroup sparsityspatial groupingspectral dataalgorithm convergencealternating direction method of multipliersimage processing
Authors
Quan Yu, Yu-Hong Dai, Xiongjun Zhang
Abstract
Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computational cost and limited flexibility in characterizing spatially structured anomalies. To address these issues, this paper proposes a hyperspectral anomaly detection method based on group sparse low-rank tensor factorization with automatic anomaly grouping (GSAA). Specifically, the low tubal rank background is characterized by imposing group sparsity on tensor factors, which provides an efficient alternative to direct tensor rank regularization. For anomaly modeling, a latent grouping map is introduced to build an automatic anomaly grouping penalty, allowing anomaly groups to be adaptively inferred from the data rather than predefined at the pixel level. To further exploit complementary spectral and spatial information, GSAA is applied in both domains, and the resulting detection maps are fused to form a spectral--spatial version of GSAA, termed GSAA-SS. An efficient linearized alternating direction method of multipliers algorithm with convergence guarantee is developed to solve the resulting model. Experimental results on five real hyperspectral datasets demonstrate that the proposed method achieves superior detection performance and competitive computational efficiency compared with several state-of-the-art methods.