# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
###### Abstract
This limitation has driven researchers to explore and adopt end-to-end deep learning models, leveraging data-driven approaches to reduce reliance on handcrafted feature engineering. Recently, the rapid advancements in deep learning have revitalized HTD. Deep learning can capture complex and diverse nonlinear characteristics of HSI using multi-layered network structures. Deep learning-based HTD methods can be categorized into two main approaches. The first approach constructed an end-to-end network for HSI to enable target detection across all images. [PERSON] et al. [33] developed a detection model using a 3D Convolutional Neural Network (CNN) for maritime HTD. [PERSON] et al. [34] introduced HTD-Net, a method combining an Auto Encoder (AE) with CNN for HTD in scenarios with missing labels. [PERSON] et al. [35] introduced the HTD-ViT algorithm, which integrated spectral and spatial information for HTD. [PERSON] et al. [36] combined Fourier transform with Transformer to develop a dual-branch Fourier hybrid transform network. [PERSON] et al. [37] innovatively enhanced spectral feature learning by integrating the momentum encoder with Transformer. [PERSON] et al. [38] introduced a triplet spectral transformer method for HTD, excelling at capturing long-term dependencies in spectral features. The second approach involved reconstructing hyperspectral data using generative deep learning, followed by target detection based on differences between the reconstructed target and background. These methods typically relied on learning from background samples generated after coarse detection. With advancements in generative networks, Generative Adversarial Networks (GAN) [39], Variational Auto Encoders (VAE) [40], and AEs have been widely applied in HTD. [PERSON] et al. [41] pioneered the field by proposing a target constrained background GAN model, which relies on background learning based on target suppression constraint (BLTSC) for HTD. [PERSON] et al. [42] introduced an unsupervised algorithm leveraging a deep latent spectral representation model to adaptively map latent representations to optimal subset relationships. [PERSON] et al. [43] proposed a self-supervised background learning method by combining principal component analysis (PCA) with an adversarial autoencoder (AAE) network. [PERSON] et al. [44] proposed a Spectral Regularized Unsupervised Network (SRUN) to enhance the spectral representation capabilities of Autoencoder and Variational Autoencoder (VAE) models. The framework integrated spectral angular difference-driven feature selection and adaptive weighting mechanisms to generate discriminative detection maps. [PERSON] et al. [45] combined OSP theory with VAE and proposed the OS-VAE network framework, exploring new methods that fuse traditional methods with deep learning.In order to improve non-linear feature expression and physical interpretability, [PERSON] et al. [46] designed a subspace representation network for HTD. [PERSON] et al. successively integrated AE with HTD to develop the DCSSAE network [47] and 3 DMMRAE network [48]. Despite the significant success of deep learning-based HTD, several challenges remain:  
1. Supervised deep learning often demands extensive labeled training data, which is challenging to obtain in practical scenarios, potentially undermining its robustness. Recently, self-supervised generative HTD has advanced significantly, but coarse detection often suffers from background-target misclassification. Improving coarse detection remains a critical challenge in generative HTD. 2. Effective background learning is crucial for enhancing algorithm performance through improved background representation. However, HSI is often underestimated in practical applications, with its relationship to prior spectra poorly understood, leading to inadequate suppression of specific targets.