All Outputs (20)

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..

Horizons in imaging. (2024)
Journal Article
PIVA, A., ZHANG, L. and REN, J. 2024. Horizons in imaging. Frontiers in imaging [online], 3, article number 1530335. Available from: https://doi.org/10.3389/fimag.2024.1530335

Over the past several years, imaging technology has undergone a rapid wave of innovation, with new applications reshaping a wide range of fields, from healthcare and environmental monitoring to entertainment and civil security. This rapid progression... Read More about Horizons in imaging..

ICSF: integrating inter-modal and cross-modal learning framework for self-supervised heterogeneous change detection. (2024)
Journal Article
ZHANG, E., ZONG, H., LI, X., FENG, M. and REN, J. 2025. ICSF: integrating inter-modal and cross-modal learning framework for self-supervised heterogeneous change detection. IEEE transactions on geoscience and remote sensing [online], 63, 501516. Available from: https://doi.org/10.1109/TGRS.2024.3519195

Heterogeneous change detection (HCD) is a process to determine the change information by analyzing heterogeneous images of the same geographic location taken at different times, which plays an important role in remote sensing applications such as dis... Read More about ICSF: integrating inter-modal and cross-modal learning framework for self-supervised heterogeneous change detection..

Dual teacher: improving the reliability of pseudo labels for semi-supervised oriented object detection. (2024)
Journal Article
FANG, Z., REN, J., ZHENG, J., CHEN, R. and ZHAO, H. 2025. Dual teacher: improving the reliability of pseudo labels for semi-supervised oriented object detection. IEEE transactions on geoscience and remote sensing [online], 63, 5602515. Available from: https://doi.org/10.1109/TGRS.2024.3519173

Oriented object detection in remote sensing is a critical task for accurately location and measurement of the interested targets. Despite of its success in object detection, deep learning-based detectors rely heavily on extensive data annotation. How... Read More about Dual teacher: improving the reliability of pseudo labels for semi-supervised oriented object detection..

Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain. (2024)
Journal Article
TOLIE, H.F., REN, J., CHEN, R., ZHAO, H. and ELYAN, E. 2025. Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain. Engineering applications of artificial intelligence [online], 141, article number 109730. Available from: https://doi.org/10.1016/j.engappai.2024.109730

In subsea environments, sound navigation and ranging (SONAR) images are widely used for exploring and monitoring infrastructures due to their robustness and insensitivity to low-light conditions. However, their quality can degrade during acquisition... Read More about Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain..

High-resolution remote sensing image change detection based on Fourier feature interaction and multi-scale perception. (2024)
Journal Article
CHEN, Y., FENG, S., ZHAO, C., SU, N., LI, W., TAO, R. and REN, J. 2024. High-resolution remote sensing image change detection based on Fourier feature interaction and multi-scale perception. IEEE transactions on geoscience and remote sensing [online], 62, article number 3500073. Available from: https://doi.org/10.1109/TGRS.2024.3500073

As a significant means of Earth observation, change detection in high-resolution remote sensing images has received extensive attention. Nevertheless, the variability in imaging conditions introduces style discrepancies and a range of pseudo change r... Read More about High-resolution remote sensing image change detection based on Fourier feature interaction and multi-scale perception..

Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging. (2024)
Journal Article
HUANG, M., TANG, Y., TAN, Z., REN, J., HE, Y. and HUANG, H. 2024. Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging. Infrared physics and technology [online], 143, article number 105625. Available from: https://doi.org/10.1016/j.infrared.2024.105625

The quality of black tea significantly relies on its fermentation process. Nevertheless, achieving precise and objective evaluations remains challenging due to the subjective nature of manual judgment involved in quality monitoring. To address this p... Read More about Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging..

MDAR: a multiscale features-based network for remotely measuring human heart rate utilizing dual-branch architecture and alternating frame shifts in facial videos. (2024)
Journal Article
ZHANG, L., REN, J., ZHAO, S. and WU, P. 2024. MDAR: a multiscale features-based network for remotely measuring human heart rate utilizing dual-branch architecture and alternating frame shifts in facial videos. Sensors [online], 24(21), article number 6791. Available from: https://doi.org/10.3390/s24216791

Remote photoplethysmography (rPPG) refers to a non-contact technique that measures heart rate through analyzing the subtle signal changes of facial blood flow captured by video sensors. It is widely used in contactless medical monitoring, remote heal... Read More about MDAR: a multiscale features-based network for remotely measuring human heart rate utilizing dual-branch architecture and alternating frame shifts in facial videos..

HyperDehazing: a hyperspectral image dehazing benchmark dataset and a deep learning model for haze removal. (2024)
Journal Article
FU, H., LING, Z., SUN, G., REN, J., ZHANG, A., ZHANG, L. and JIA, X. 2024. HyperDehazing: a hyperspectral image dehazing benchmark dataset and a deep learning model for haze removal. ISPRS journal of photogrammetry and remote sensing [online], 218(part A), pages 663-677. Available from: https://doi.org/10.1016/j.isprsjprs.2024.09.034

Haze contamination severely degrades the quality and accuracy of optical remote sensing (RS) images, including hyperspectral images (HSIs). Currently, there are no paired benchmark datasets containing hazy and haze-free scenes in HSI dehazing, and fe... Read More about HyperDehazing: a hyperspectral image dehazing benchmark dataset and a deep learning model for haze removal..

Hyperspectral imagery quality assessment and band reconstruction using the Prophet model. (2024)
Journal Article
MA, P., REN, J., GAO, Z., LI, Y. and CHEN, R. [2024]. Hyperspectral imagery quality assessment and band reconstruction using the Prophet model. CAAI transactions on intelligence technology [online], Early View. Available from: https://doi.org/10.1049/cit2.12373

In Hyperspectral Imaging (HSI), the detrimental influence of noise and distortions on data quality is profound, which has severely affected the following-on analytics and decision-making such as land mapping. This study presents an innovative framewo... Read More about Hyperspectral imagery quality assessment and band reconstruction using the Prophet model..

GaitAE: a cognitive model-based autoencoding technique for gait recognition. (2024)
Journal Article
LI, R., LI, H., QIU, Y., REN, J., NG, W.W.Y. and ZHAO, H. 2024. GaitAE: a cognitive model-based autoencoding technique for gait recognition. Mathematics [online], 12(17), article number 2780. Available from: https://doi.org/10.3390/math12172780

Gait recognition is a long-distance biometric technique with significant potential for applications in crime prevention, forensic identification, and criminal investigations. Existing gait recognition methods typically introduce specific feature refi... Read More about GaitAE: a cognitive model-based autoencoding technique for gait recognition..

Two-click based fast small object annotation in remote sensing images. (2024)
Journal Article
LEI, L., FANG, Z., REN, J., GAMBA, P., ZHENG, J. and ZHAO, H. 2024. Two-click based fast small object annotation in remote sensing images. IEEE transactions of geoscience and remote sensing [online], 62, article number 5639513. Available from: https://doi.org/10.1109/tgrs.2024.3442732

In the remote sensing field, detecting small objects is a pivotal task, yet achieving high performance in deep learning-based detectors heavily relies on extensive data annotation. The challenge intensifies as small objects in remote sensing imagery... Read More about Two-click based fast small object annotation in remote sensing images..

SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection. (2024)
Journal Article
LI, Y., REN, J., YAN, Y., SUN, G. and MA, P. 2024. SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection. Remote sensing [online], 16(3), article number 2353. Available from: https://doi.org/10.3390/rs16132353

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attrib... Read More about SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection..

Prompting-to-distill semantic knowledge for few-shot learning. (2024)
Journal Article
JI, H., GAO, Z., REN, J., WANG, X.-A., GAO, T., SUN, W. and MA, P. 2024. Prompting-to-distill semantic knowledge for few-shot learning. IEEE geoscience and remote sensing letters [online], 21, article 6011605. Available from: https://doi.org/10.1109/lgrs.2024.3414505

Recognizing visual patterns in low-data regime necessitates deep neural networks to glean generalized representations from limited training samples. In this paper, we propose a novel few-shot classification method, namely ProDFSL, leveraging multi-mo... Read More about Prompting-to-distill semantic knowledge for few-shot learning..

ABBD: accumulated band-wise binary distancing for unsupervised parameter-free hyperspectral change detection. (2024)
Journal Article
LI, Y., REN, J., YAN, Y., MA, P., ASSAAD, M. and GAO, Z. 2024. ABBD: accumulated band-wise binary distancing for unsupervised parameter-free hyperspectral change detection. IEEE journal of selected topics in applied earth observations and remote sensing [online], 17, pages 9880-9893. Available from: https://doi.org/10.1109/JSTARS.2024.3407212

As a fundamental task in remote sensing earth observation, hyperspectral change detection (HCD) aims to identify the changed pixels in bi-temporal hyperspectral images (HSIs). However, the water-absorption effect, poor weather conditions, noise and i... Read More about ABBD: accumulated band-wise binary distancing for unsupervised parameter-free hyperspectral change detection..

Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning. (2024)
Journal Article
YAN, Y., REN, J., SUN, H. and WILLIAMS, R. 2024. Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning. IEEE transactions on industrial informatics [online], 20(8), pages 9963-9975. Available from: https://doi.org/10.1109/TII.2024.3384609

Measuring the purity of the metal powder is essential to maintain the quality of additive manufacturing products. Contamination is a significant concern, leading to cracks and malfunctions in the final products. Conventional assessment methods focus... Read More about Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning..

DICAM: deep inception and channel-wise attention modules for underwater image enhancement. (2024)
Journal Article
FARHADI TOLIE, H., REN, J. and ELYAN, E. 2024. DICAM: deep inception and channel-wise attention modules for underwater image enhancement. Neurocomputing [online], 584, article number 127585. Available from: https://doi.org/10.1016/j.neucom.2024.127585

In underwater environments, imaging devices suffer from water turbidity, attenuation of lights, scattering, and particles, leading to low quality, poor contrast, and biased color images. This has led to great challenges for underwater condition monit... Read More about DICAM: deep inception and channel-wise attention modules for underwater image enhancement..

Detection-driven exposure-correction network for nighttime drone-view object detection. (2024)
Journal Article
XI, Y., JIA, W., MIAO, Q., FENG, J., REN, J. and LUO, H. 2024. Detection-driven exposure-correction network for nighttime drone-view object detection. IEEE transactions on geoscience and remote sensing [online], 62, article number 5605014. Available from: https://doi.org/10.1109/TGRS.2024.3351134

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detecti... Read More about Detection-driven exposure-correction network for nighttime drone-view object detection..

Feature aggregation and region-aware learning for detection of splicing forgery. (2024)
Journal Article
XU, Y., ZHENG, J., REN, J. and FANG, A. 2024. Feature aggregation and region-aware learning for detection of splicing forgery. IEEE signal processing letters [online], 31, pages 696-700. Available from: https://doi.org/10.1109/LSP.2023.3348689

Detection of image splicing forgery become an increasingly difficult task due to the scale variations of the forged areas and the covered traces of manipulation from post-processing techniques. Most existing methods fail to jointly multi-scale local... Read More about Feature aggregation and region-aware learning for detection of splicing forgery..