Professor Jinchang Ren's Outputs (176)

GLVMamba: a global-local visual state space model for remote sensing image segmentation. (2025)
Journal Article
LI, H., PAN, H., LIU, X., REN, J., DU, Z. and CAO, J. 2025. GLVMamba: a global-local visual state space model for remote sensing image segmentation. IEEE transactions on geoscience and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/TGRS.2025.3572127

Semantic segmentation of remote sensing images has significant advances with the adoption of deep neural networks, taking the advantages of Convolutional Neural Networks (CNNs) in local feature extraction with Transformers in global information model... Read More about GLVMamba: a global-local visual state space model for remote sensing image segmentation..

Blind quality assessment using channel-based structural, dispersion rate scores, and overall saturation and hue for underwater images. (2025)
Journal Article
TOLIE, H.F., REN, J., CAI, J., CHEN, R. and ZHAO, H. 2025. Blind quality assessment using channel-based structural, dispersion rate scores, and overall saturation and hue for underwater images. IEEE journal of oceanic engineering [online], Early Cite. Available from: https://doi.org/10.1109/joe.2025.3553888

In underwater subsea environments light attenuation, water turbidity, and limitations of the optical devices make the captured images suffer from poor contrast and quality, proportional degradation, low visibility, and low color richness. In recent y... Read More about Blind quality assessment using channel-based structural, dispersion rate scores, and overall saturation and hue for underwater images..

Low-rank and sparse representation meet deep unfolding: a new interpretable network for hyperspectral change detection. (2025)
Journal Article
ZHOU, C., HE, Z., DONG, J., LI, Y., REN, J. and PLAZA, A. 2025. Low-rank and sparse representation meet deep unfolding: a new interpretable network for hyperspectral change detection. IEEE transactions on geoscience and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/TGRS.2025.3564996

Hyperspectral image change detection (HSI-CD) is a technique that intelligently checks the changed details in bitemporal hyperspectral images (Bi-HSIs). Deep learning (DL), with the ability to model nonlinear changing features, has achieved promising... Read More about Low-rank and sparse representation meet deep unfolding: a new interpretable network for hyperspectral change detection..

Prototype-guided spatial-spectral interaction network for hyperspectral anomaly detection. (2025)
Journal Article
CHENG, X., WANG, C., HUO, Y., ZHANG, M., WANG, H. and REN, C. [2025]. Prototype-guided spatial-spectral interaction network for hyperspectral anomaly detection. IEEE transactions on geoscience and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/TGRS.2025.3568121

In recent years, deep learning has emerged as one of the most widely utilized techniques in hyperspectral anomaly detection (HAD) with an impressive detection accuracy. However, the investigation into the diverse background representation and the spa... Read More about Prototype-guided spatial-spectral interaction network for hyperspectral anomaly detection..

LKVHAN: multi-scale large kernel vertical-horizontal attention network for hyperspectral image classification. (2025)
Journal Article
LIU, X., NG, A.H.-M., LIAO, X., LEI, F., REN, J. and GE, L. [2025]. LKVHAN: multi-scale large kernel vertical-horizontal attention network for hyperspectral image classification. IEEE journal of selected topics in applied earth observations and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/JSTARS.2025.3567742

Among deep learning-based hyperspectral image (HSI) classification models, convolutional neural networks (CNNs), Transformers, Mamba, and large kernel CNNs (LKCNNs) models have been widely explored for HSI classification. Nonetheless, these models su... Read More about LKVHAN: multi-scale large kernel vertical-horizontal attention network for hyperspectral image classification..

MSLKCNN: a simple and powerful multi-scale large kernel CNN for hyperspectral image classification. (2025)
Journal Article
LIU, X., NG, A.H.-M., LEI, F., REN, J. GUO, L. and DU, Z. [2025]. MSLKCNN: a simple and powerful multi-scale large kernel CNN for hyperspectral image classification. IEEE transactions on geoscience and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/TGRS.2025.3566616

Deep learning-based hyperspectral image (HSI) classification models typically utilize multiple feature extraction layers to learn the features of land covers. Nevertheless, they encounter challenges, e.g., 1) Transformers require substantial computat... Read More about MSLKCNN: a simple and powerful multi-scale large kernel CNN for hyperspectral image classification..

Unsupervised domain adaptation for VHR urban scene segmentation via prompted foundation model-based hybrid training joint-optimized network. (2025)
Journal Article
LYU, S., ZHAO, Q., SUN, Y., CHENG, G., HE, Y., WANG, G., REN, J. and SHI, Z. 2025. Unsupervised domain adaptation for VHR urban scene segmentation via prompted foundation model based hybrid training joint-optimized network. IEEE transactions on geoscience and remote sensing [online], 63, article number 4409117. Available from: https://doi.org/10.1109/tgrs.2025.3564216

Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-RSSeg) is to adapt a model trained on the source domain data to the target domain samples, thereby minimizing the need for annotated data across diverse remote sensing scene... Read More about Unsupervised domain adaptation for VHR urban scene segmentation via prompted foundation model-based hybrid training joint-optimized network..

Hyperspectral image classification using a multi-scale CNN architecture with asymmetric convolutions from small to large kernels. (2025)
Journal Article
LIU, X., NG, A.H.-M., LEI, F., REN, J., LIAO, X. and GE, L. 2025. Hyperspectral image classification using a multi-scale CNN architecture with asymmetric convolutions from small to large kernels. Remote sensing [online], 17(8), article number 1461. Available from: https://doi.org/10.3390/rs17081461

Deep learning-based hyperspectral image (HSI) classification methods, such as Transformers and Mambas, have attracted considerable attention. However, several challenges persist, e.g., (1) Transformers suffer from quadratic computational complexity d... Read More about Hyperspectral image classification using a multi-scale CNN architecture with asymmetric convolutions from small to large kernels..

FusDreamer: label-efficient remote sensing world model for multimodal data classification. (2025)
Journal Article
WANG, J., SONG, W., CHEN, H., REN, J. and ZHAO, H. 2025. FusDreamer: label-efficient remote sensing world model for multimodal data classification. IEEE transactions on geoscience and remote sensing [online], 63, article number 570314. Available from: https://doi.org/10.1109/TGRS.2025.3554862

World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing worl... Read More about FusDreamer: label-efficient remote sensing world model for multimodal data classification..

Entropy guidance hierarchical rich-scale feature network for remote sensing image semantic segmentation of high resolution. (2025)
Journal Article
ZHANG, H., LI, L., XIE, X., HE, Y., REN, J. and XIE, G. 2025. Entropy guidance hierarchical rich-scale feature network for remote sensing image semantic segmentation of high resolution. Applied intelligence [online], 55(6), article number 528. Available from: https://doi.org/10.1007/s10489-025-06433-1

Semantic segmentation of high-resolution remote sensing images (HRRSIs) is crucial for a wide range of applications, such as urban planning and disaster management. However, in HRRSIs, existing multiscale feature extraction and fusion methods often f... Read More about Entropy guidance hierarchical rich-scale feature network for remote sensing image semantic segmentation of high resolution..

MDDNet: multilevel difference-enhanced denoise network for unsupervised change detection in SAR images. (2025)
Presentation / Conference Contribution
ZONG, H., ZHANG, E., LI, X., ZHANG, H. and REN, J. 2025. MDDNet: multilevel difference-enhanced denoise network for unsupervised change detection in SAR images. In Proceedings of the 50th IEEE (Institute of Electrical and Electronics Engineers) International conference on acoustics, speech and signal processing 2025 (ICASSP 2025), 6-11 April 2025, Hyderabad, India. Piscataway: IEEE [online], article number 576. Available from: https://doi.org/10.1109/icassp49660.2025.10887943

Change detection in synthetic aperture radar (SAR) images is a hot yet highly challenging task in remote sensing. Existing unsupervised SAR change detection methods often struggle with inherent speckle noise and insufficiently utilize pseudo-labels,... Read More about MDDNet: multilevel difference-enhanced denoise network for unsupervised change detection in SAR images..

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..

Gf-former: an accurate UAV-based remote sensing image network for high-precision automatic segmentation of ground fissures in mining regions. (2025)
Journal Article
MENG, J., XU, X., LI, P., ZHANG, Z., ZHAO, W., REN, J. and LI, Y. 2025. Gf-former: an accurate UAV-based remote sensing image network for high-precision automatic segmentation of ground fissures in mining regions. International journal of machine learning and cybernetics [online], Latest Articles. Available from: https://doi.org/10.1007/s13042-025-02555-7

Ground fissure information is critical for ensuring the safety of mining operations and preventing geological disasters. Challenges include obscuration by vegetation or shadows and varying fissure sizes. To address these, we introduce GF-Former, a sp... Read More about Gf-former: an accurate UAV-based remote sensing image network for high-precision automatic segmentation of ground fissures in mining regions..

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], 63, article number 5612611. 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], 63, article number 5616119. 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..

Protecting visual data privacy in offshore industry via underwater image inpainting. (2024)
Presentation / Conference Contribution
TOLIE, H.F., REN, J., CHEN, R. and ZHAO, H. 2024. Protecting visual data privacy in offshore industry via underwater image inpainting. In Proceedings of the 9th International conference on image, vision and computing 2024 (ICIVC 2024), 15-17 July 2024, Suzhou, China. Piscataway: IEEE [online], pages 281-286. Available from: https://doi.org/10.1109/ICIVC61627.2024.10837433

Leveraging advanced artificial intelligence (AI) methodologies offers the advantage of incorporating multiple expert viewpoints, thereby facilitating a more comprehensive inspection of underwater infrastructure. However, the implementation of AI tech... Read More about Protecting visual data privacy in offshore industry via underwater image inpainting..

Special issue on the application of remote sensing spatio-temporal big data to effective environmental monitoring and sustainable development. (2024)
Journal Article
SUN, G., REN, J., SUN, Q. and JIA, M. 2024. Special issue on the application of remote sensing spatio-temporal big data to effective environmental monitoring and sustainable development. Journal of geodesy and geoinformation science [online], 7(4), pages 2-3. Available from: https://doi.org/10.11947/j.JGGS.2024.0401

The rapid advancement of remote sensing, data science, and geographic information technology has ushered in an era of substantial information explosion. Massive spatio-temporal big data provides rich data resources and technical means have facilitate... Read More about Special issue on the application of remote sensing spatio-temporal big data to effective environmental monitoring and sustainable development..

Effective marine monitoring with multimodal sensing and improved underwater robotic perception towards environmental protection and smart energy transition. (2024)
Journal Article
FARHADI TOLIE, H., REN, J., HASAN, M.J., MA, P, KENNAN, S. and LI, Y. 2024. Effective marine monitoring with multimodal sensing and improved underwater robotic perception towards environmental protection and smart energy transition. Journal of geodesy and geoinformation science [online], 7(4), pages 19-35. Available from: https://doi.org/10.11947/j.JGGS.2024.0403

Effective underwater sensing is crucial for environmental protection and sustainable energy transitions, particularly as we face growing challenges in marine ecosystem monitoring, resource management, and the need for efficient energy infrastructure.... Read More about Effective marine monitoring with multimodal sensing and improved underwater robotic perception towards environmental protection and smart energy transition..