Qianqian Duan

Xing Hu, Xiangcheng Liu, Qianqian Duan et al.

Hyperspectral image (HSI) analysis plays a critical role in remote sensing, agriculture, and environmental monitoring. However, traditional methods often struggle to handle the high dimensionality, spectral redundancy, and noise inherent in HSI data, limiting their accuracy and scalability. Recently, diffusion models including denoising diffusion probabilistic models and other generative frameworks based on stochastic differential equations have shown strong potential in capturing complex spectral spatial structures and generating high fidelity HSI data. These models offer effective solutions for tasks such as noise supression, data augmentation, classification, and anomaly detection. This review presents a systematic summary of recent advances in diffusion models for HSI processing. We categorize existing methods, highlight their strengths in handling high dimensional data, and compare their performance with conventional approaches. Special attention is given to critical applications such as change detection and post disaster anomaly identification. The review also discusses current limitations, such as computational cost and training stability, and outlines potential research directions. Our main contributions can be summarized as follows: we provide a systematic taxonomy of diffusion based HSI methods, examine their applications across major remote sensing tasks, and offer perspectives on potential directions for future research. With these efforts, this review seeks to support the community in harnessing deep learning models to achieve more effective and efficient hyperspectral image analysis.

Xing Hu, Haodong Chen, Qianqian Duan et al.

With the global population increasing and arable land resources becoming increasingly limited, smart and precision agriculture have emerged as essential directions for sustainable agricultural development. Artificial intelligence (AI), particularly deep learning models, has been widely adopted in applications such as crop monitoring, pest detection, and yield prediction. Among recent generative models, diffusion models have demonstrated considerable potential in agricultural image processing, data augmentation, and remote sensing analysis. Compared to traditional generative adversarial networks (GANs), diffusion models exhibit greater training stability and superior image generation quality, effectively addressing challenges such as limited annotated datasets and imbalanced sample distributions in agricultural scenarios. This paper reviews recent advancements in the application of diffusion models within agriculture, focusing on their roles in crop disease and pest detection, remote sensing image enhancement, crop growth prediction, and agricultural resource management. Diffusion models have been found useful in improving tasks like image generation, denoising, and data augmentation in agriculture, especially when environmental noise or variability is present. While their high computational requirements and limited generalizability across domains remain concerns, the approach is gradually proving effective in real-world applications such as precision crop monitoring. As research progresses, these models may help support sustainable agriculture and address emerging challenges in food systems.