ProTD: Prompted Tensor Decomposition for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background in hyperspectral images (HSIs). Recently, low-rank representation (LRR)-based methods have attracted widespread attention, but their pixel-level design overlooks semantic information in HSIs, resulting in unsatisfactory anomaly detection performance. To address this issue, we propose a prompted tensor decomposition (ProTD) for HAD, which cleverly leverages the power of large vision models with the designed prompt to guide the decomposition of HSIs. Specifically, guided by high-level semantic information distilled from a pretrained Segment Anything Model (SAM) with the prompt, we leverage uni-deep Tucker decomposition with Spatio-Spectral Total Variation (SSTV) regularization to represent the background, while using ℓ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm to capture anomalies. To solve this model, we design an efficient algorithm based on alternating minimization. Extensive experiments on benchmark datasets (including Airport, Urban, and Beach) demonstrate that the proposed ProTD model outperforms state-of-the-art HAD methods, especially those LRR-based methods.