Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective

Abstract

In data acquisition and transmission, hyperspectral images are inevitably corrupted by additive noises, making it challenging to accurately observe and recognize the materials on the surface of the Earth. However, scholars have been addicted to developing numerous complex methods for separable two-stage denoising and anomaly detection (AD) tasks over the past years, rarely paying attention to the real effect of noises for subsequent intelligent interpretation. To this date, we propose a hierarchical integration framework for hyperspectral simultaneous AD and denoising (HyADD). Joint AD and denoising are mutually integrated and their outputs in each iteration stimulate each other, breaking through the respective performance bottlenecks of the separable two-stage scheme. Inspired by spatial–spectral gradient domain-based constraint, HyADD removes additive noises and preserves advantageous image smoothness information to improve intermediate detection performances in the iteration loop. Conversely, with the assistance of the antinoise dictionary conduction and the subspace domain-based low-rankness, the identification of anomalies with different features from the background can provide effective feedback to the denoising process. The proposed algorithm is efficiently solved by the well-designed linearized alternating direction method of multipliers with an adaptive penalty. A comparison with the existing well-known AD algorithms via simulated and real-world experiments establishes the competitiveness of the proposed HyADD with state-of-the-art methods.

Keywords

• Anomaly detection (AD)
• denoising
• feedback
• integration framework
• linearized alternating direction method of multipliers