All Outputs (154)

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..

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..

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..

Promptable sonar image segmentation for distance measurement using SAM. (2024)
Presentation / Conference Contribution
TOLIE, H.F., REN, J., HASAN, M.J., KANNAN, S. and FOUGH, N. 2024. Promptable sonar image segmentation for distance measurement using SAM. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea; learning to measure sea health parameters (IEEE MetroSea 2024), 14-16 October 2024, Portorose, Slovenia. Piscataway: IEEE [online], pages 229-233. Available from: https://doi.org/10.1109/metrosea62823.2024.10765703

The subsea environment presents numerous challenges for robotic vision, including non-uniform light attenuation, backscattering, floating particles, and low-light conditions, which significantly degrade underwater images. This degradation impacts rob... Read More about Promptable sonar image segmentation for distance measurement using SAM..

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..

Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising. (2024)
Presentation / Conference Contribution
LI, Y., REN, J., GAO, Z. and SUN, G. 2024. Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising. In Proceedings of the 2024 IEEE International geoscience and remote sensing symposium (IGARSS 2024), Athens, Greece, 7-12 July 2024. Piscataway: IEEE [online], pages 9210-9213. Available from: https://doi.org/10.1109/igarss53475.2024.10641314

As an effective tool for monitoring surface irregularities in remote sensing, hyperspectral anomaly detection (HAD) has garnered increasing attention. However, how to improve the detection accuracy remains a formidable challenge, due mainly to the no... Read More about Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising..

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..

Enrich, distill and fuse: generalized few-shot semantic segmentation in remote sensing leveraging foundation model's assistance. (2024)
Presentation / Conference Contribution
GAO, T., AO, W., WANG, X.-A., ZHAO, Y., MA, P., XIE, M., FU, H., REN, J. and GAO, Z. 2024. Enrich, distill and fuse: generalized few-shot semantic segmentation in remote sensing leveraging foundation model’s assistance. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Computer Society conference on Computer vision and pattern recognition workshops (CVPRW 2024), 16-22 June 2024, Seattle, WA, USA. Piscataway: IEEE [online], pages 2771-2780. Available from: https://doi.org/10.1109/CVPRW63382.2024.00283

Generalized few-shot semantic segmentation (GFSS) unifies semantic segmentation with few-shot learning, showing great potential for Earth observation tasks under data scarcity conditions, such as disaster response, urban planning, and natural resourc... Read More about Enrich, distill and fuse: generalized few-shot semantic segmentation in remote sensing leveraging foundation model's assistance..

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..

MLM-LSTM: multi-layer memory learning framework based on LSTM for hyperspectral change detection. (2024)
Presentation / Conference Contribution
LI, Y., YAN, Y. and REN, C., LIU, Q. and SUN, H. 2024. MLM-LSTM: multi-layer memory learning framework based on LSTM for hyperspectral change detection. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 51-61. Available from: https://doi.org/10.1007/978-981-97-1417-9_5.

Hyperspectral change detection plays a critical role in remote sensing by leveraging spectral and spatial information for accurate land cover variation identification. Long short-term memory (LSTM) has demonstrated its effectiveness in capturing depe... Read More about MLM-LSTM: multi-layer memory learning framework based on LSTM for hyperspectral change detection..

HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion. (2024)
Presentation / Conference Contribution
WU, Y., ZHANG, X., LIU, Q., XUE, D., SUN, H. and REN, J. 2024. HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 251-263. Available from: https://doi.org/10.1007/978-981-97-1417-9_24

Multi-object tracking in satellite videos (SV-MOT) is one of the most challenging tasks in remote sensing, its difficulty mainly comes from the low spatial resolution, small target and extremely complex background. The widely studied multi-object tra... Read More about HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion..

Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. (2024)
Presentation / Conference Contribution
HASAN, M.J., ELYAN, E., YAN, Y., REN, J. and SARKER, M.M.K. 2024. Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 220-228. Available from: https://doi.org/10.1007/978-981-97-1417-9_21

Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to identify the heat los... Read More about Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies..