Skip to main content

Research Repository

Advanced Search

All Outputs (9)

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..

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..

Image enhancement for UAV visual SLAM applications: analysis and evaluation. (2024)
Presentation / Conference Contribution
TIAN, Y., YUE, H. and REN, J. 2024. Image enhancement for UAV visual SLAM applications: analysis and evaluation. 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_20.

Although simultaneous localisation and mapping (SLAM) has been widely applied in a wide range of robotics and navigation applications, its applicability is severely affected by the quality of the acquired images, especially for those in unmanned aeri... Read More about Image enhancement for UAV visual SLAM applications: analysis and evaluation..

Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset. (2024)
Presentation / Conference Contribution
YAN, Y., LI, Y., LIN, H., SARKER, M.M.K., REN, J. and MCCALL, J. 2024. Underwater object detection for smooth and autonomous operations of naval missions: a pilot dataset. 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 113-122. Available from: https://doi.org/10.1007/978-981-97-1417-9_11

Underwater object detection is essential for ensuring autonomous naval operations. However, this task is challenging due to the complexities of underwater environments that often degrade image quality, thereby hampering the performance of detection a... Read More about Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset..

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..

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..

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..