# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
###### Abstract  
Hyperspectral Imagery (HSI) plays a crucial role in military and civilian target detection. However, HSI target detection remains highly challenging due to the interference caused by complex and diverse real-world scenarios. This paper proposes a Spectral Information Divergence-driven Diffusion Network model (SID-DN) for hyperspectral target detection, which significantly enhances detection robustness in complex scenes by decoupling background distribution modeling from target detection. The proposed method focuses on learning the background distribution in hyperspectral data and achieves target detection by accurately reconstructing background samples to identify differences between background and target samples. This method introduces an adaptive coarse detection module, which optimizes the coarse detection process in generative hyperspectral target detection, effectively reducing the background-target misclassification. Additionally, a SID-based Diffusion model is designed to optimize the loss of Diffusion, effectively reducing the interference of suspected target samples during the background learning process. Experiments on three real-world datasets demonstrate that the method is highly competitive, with detection results significantly outperforming current state-of-the-art methods. Hyperspectral Imagery; background learning; diffusion; spectral information divergence; target detection +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00  
Hyperspectral Target Detection (HTD) identifies targets in complex backgrounds by exploiting subtle spectral differences between the targets and backgrounds in HSI. Because of impediments such as cloud cover and the poor spatial resolution of HSI, detected targets are often represented at a sub-pixel level, making the extraction process challenging. HTD methods are primarily divided into classical detection techniques and deep learning-based methods. The classical HTD methods primarily include methods based on spectral angle detection [12], hypothesis testing [13], Constrained Energy Minimization (CEM) [14], signal decomposition [15] and sparse representation [16]. Spectral angle detection methods measure spectral similarity through geometric or probabilistic metrics, such as Spectral Angle Mapper (SAM) [12], Adaptive Coherence Estimator(ACE) [17] and Spectral Information Divergence (SID) [18].These methods are sensitive to spectral variations and often serve as pre- or post-processing steps due to their computational simplicity [19].Hypothesis testing-based detectors, including Adaptive Spatial Detector (ASD) [20] and Kernel-based Adaptive Subspace Detector (KASD) [21], rely on statistical signal theory and likelihood ratio tests but face limitations from Gaussian distribution assumptions. The CEM detection algorithm [14], minimizes background energy while enhancing target responses through linear filtering. Though simple, its linear nature struggles with nonlinear HSI features, prompting nonlinear extensions like hCEM [22] and machine learning variants [23; 24].The Signal Decomposition Detection method [25; 26] separate targets from backgrounds using models like Orthogonal Subspace Projection (OSP) [27] and Low-Rank and Sparse Matrix Decomposition (LRaSMD) [28], yet require precise spectral modeling and suffer from high complexity.The sparse representation methods [29; 30; 31] leverage sparse coefficients and dictionary learning (e.g., a dual sparsity constrained(DSC) [32]) to enhance target-background discrimination. While effective, their performance depends heavily on dictionary completeness, limiting generalization.Most traditional detection algorithms rely on manually predefined assumptions (e.g., spectral matching criteria, energy minimization, or distribution models) and struggle to effectively handle the nonlinear characteristics inherent in complex real-world scenarios.