All Outputs (5)

Binary quantization vision transformer for effective segmentation of red tide in multi-spectral remote sensing imagery. (2025)
Journal Article
XIE, Y., HOU, X., REN, J., ZHANG, X., MA, C. and ZHENG, J. 2025. Binary quantization vision transformer for effective segmentation of red tide in multi-spectral remote sensing imagery. IEEE Transactions on geoscience and remote sensing [online], 63, article number 4202814. Available from: https://doi.org/10.1109/TGRS.2025.3540784

As a global marine disaster, red tides pose serious threats to marine ecology and the blue economy, making their monitoring crucial for preventing harmful algal blooms and protecting the marine environment. In this study, satellite remote sensing was... Read More about Binary quantization vision transformer for effective segmentation of red tide in multi-spectral remote sensing imagery..

An optimized lightweight real-time detection network model for IoT embedded devices. (2025)
Journal Article
CHEN, R., WANG, P., LIN, B., WANG, L., ZENG, X., HU, X., YUAN, J., LI, J., REN, J. and ZHAO, H. 2025. An optimized lightweight real-time detection network model for IoT embedded devices. Scientific reports [online], 15(1), article number 3839. Available from: https://doi.org/10.1038/s41598-025-88439-w

With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, a... Read More about An optimized lightweight real-time detection network model for IoT embedded devices..

Frequency-domain guided swin transformer and global-local feature integration for remote sensing images semantic segmentation. (2025)
Journal Article
ZHANG, H., XIE, G., LI, L., XIE, X. and REN, J. [2025]. Frequency-domain guided swin transformer and global-local feature integration for remote sensing images semantic segmentation. IEEE Transactions on geoscience and remote sensing [online], (Early Access). Available from: https://doi.org/10.1109/TGRS.2025.3535724

Convolutional Neural Networks (CNNs), transformers, and the hybrid methods have been significant application in remote sensing. However, existing methods are limited in effectively modeling frequency domain information, which affects their ability to... Read More about Frequency-domain guided swin transformer and global-local feature integration for remote sensing images semantic segmentation..

ChangeDA: depth-augmented multi-task network for remote sensing change detection via differential analysis. (2025)
Journal Article
MENG, J., XU, X., ZHANG, Z., LI, P., XIE, G., REN, J. and ZHENG, Y. [2025]. ChangeDA: depth-augmented multi-task network for remote sensing change detection via differential analysis. IEEE Transactions on geoscience and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/TGRS.2025.3532468

In the field of Remote Sensing Change Detection (RSCD), accurately identifying significant changes between bitemporal images is essential for environmental monitoring, urban planning, and disaster assessment. In recent years, advancements in deep lea... Read More about ChangeDA: depth-augmented multi-task network for remote sensing change detection via differential analysis..

GASSM: global attention and state space model based end-to-end hyperspectral change detection. (2025)
Journal Article
LI, Y., REN, J., FU, H. and SUN, G. 2025. GASSM: global attention and state space model based end-to-end hyperspectral change detection. Journal of the Franklin Institute [online], 362(3), article number 107424. Available from: https://doi.org/10.1016/j.jfranklin.2024.107424

As an essential task to identify anomalies and monitor changes over time, change detection enables detailed earth observation in remote sensing. By combining both the rich spectral information and spatial image, hyperspectral images (HSI) have offere... Read More about GASSM: global attention and state space model based end-to-end hyperspectral change detection..