All Outputs (102)

Urban PM2.5 concentration prediction via attention-based CNN–LSTM. (2020)
Journal Article
LI, S., XIE, G., REN, J., GUO, L., YANG, Y. and XU, X. 2020. Urban PM2.5 concentration prediction via attention-based CNN–LSTM. Applied sciences [online], 10(6), article 1953. Available from: https://doi.org/10.3390/app10061953

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the eff... Read More about Urban PM2.5 concentration prediction via attention-based CNN–LSTM..

Exemplar-supported representation for effective class-incremental learning. (2020)
Journal Article
GUO, L., XIE, G., XU, X. and REN, J. 2020. Exemplar-supported representation for effective class-incremental learning. IEEE access [online], 8, pages 51276-51284. Available from: https://doi.org/10.1109/ACCESS.2020.2980386

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural networks, where the performance decreases considerably while dealing with long sequences of new classes. To tackle this issue, in this paper, we propose a new... Read More about Exemplar-supported representation for effective class-incremental learning..

Weakly supervised conditional random fields model for semantic segmentation with image patches. (2020)
Journal Article
XU, X., XUE, Y., HAN, X., ZHANG, Z., XIE, J. and REN, J. 2020. Weakly supervised conditional random fields model for semantic segmentation with image patches. Applied sciences [online], 10(5), article 1679. Available from: https://doi.org/10.3390/app10051679

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model... Read More about Weakly supervised conditional random fields model for semantic segmentation with image patches..

Varietal classification of rice seeds using RGB and hyperspectral images. (2020)
Journal Article
FABIYI, S.D., VU, H., TACHTATZIS, C., MURRAY, P., HARLE, D., DAO, T.K., ANDONOVIC, I., REN, J. and MARSHALL, S. 2020. Varietal classification of rice seeds using RGB and hyperspectral images. IEEE access [online], 8, pages 22493-22505. Available from: https://doi.org/10.1109/ACCESS.2020.2969847

Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identif... Read More about Varietal classification of rice seeds using RGB and hyperspectral images..

MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection. (2020)
Journal Article
CHEN, W., YANG, Z., REN, J., CAO, J., CAI, N., ZHAO, H. and YUEN, P. 2020. MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection. Pattern recognition [online], 102, article 107213. Available from: https://doi.org/10.1016/j.patcog.2020.107213

Band selection plays an important role in hyperspectral imaging for reducing the data and improving the efficiency of data acquisition and analysis whilst significantly lowering the cost of the imaging system. Without the category labels, it is chall... Read More about MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection..

Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas. (2020)
Journal Article
HU, S., QIN, J., REN, J., ZHAO, H., REN, J., and HONG, H. 2020. Automatic extraction of water inundation areas using sentinel-1 data for large plain areas. Remote sensing [online], 12(2), article 243. Available from: https://doi.org/10.3390/rs12020243

Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with th... Read More about Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas..

Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. (2019)
Journal Article
GAO, L., GU, D., ZHUANG, L., REN, J., YANG, D. and ZHANG, B. 2020. Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. IEEE geoscience and remote sensing letters [online], 17(8), pages 1368-1372. Available from: https://doi.org/10.1109/LGRS.2019.2945122

Hyperspectral images (HSIs), featured by high spectral resolution over a wide range of electromagnetic spectra, have been widely used to characterize materials with subtle differences in the spectral domain. However, a large number of bands and an in... Read More about Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification..

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. (2019)
Journal Article
CUI, X., ZHENG, K., GAO, L., ZHANG, B., YANG, D. and REN, J. 2019. Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. Remote sensing [online], 11(19), article 2220. Available from: https://doi.org/10.3390/rs11192220

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features... Read More about Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification..

Deep learning based single image super-resolution: a survey. (2019)
Journal Article
HA, V.K., REN, J.-C., XU, X.-Y., ZHAO, S., XIE, G., MASERO, V. and HUSSAIN, A. 2019. Deep learning based single image super-resolution: a survey. International journal of automation and computing [online], 16(4), pages 413-426. Available from: https://doi.org/10.1007/s11633-019-1183-x

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recentl... Read More about Deep learning based single image super-resolution: a survey..

Hyperspectral image reconstruction using multi-colour and time-multiplexed LED illumination. (2019)
Journal Article
TSCHANNERL, J., REN, J., ZHAO, H., KAO, F.-J., MARSHALL, S. and YUEN, P. 2019. Hyperspectral image reconstruction using multi-colour and time-multiplexed LED illumination. Optics and lasers in engineering [online], 121, pages 352-357. Available from: https://doi.org/10.1016/j.optlaseng.2019.04.014

The rapidly rising industrial interest in hyperspectral imaging (HSI) has generated an increased demand for cost effective HSI devices. We are demonstrating a mobile and low-cost multispectral imaging system, enabled by time-multiplexed RGB Light Emi... Read More about Hyperspectral image reconstruction using multi-colour and time-multiplexed LED illumination..

Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles. (2019)
Journal Article
ZHANG, A., SUN, G., MA, P., JIA, X., REN, J., HUANG, H. and ZHANG, X. 2019. Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles. Remote sensing [online], 11(8), article 952. Available from: https://doi.org/10.3390/rs11080952

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and exten... Read More about Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles..

Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. (2019)
Journal Article
PAN, X., YANG, F., GAO, L., CHEN, Z., ZHANG, B., FAN, H. and REN, J. 2019. Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. Remote sensing [online], 11(8), article 917. Available from: https://doi.org/10.3390/rs11080917

Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper,... Read More about Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms..

A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading. (2019)
Journal Article
WANG, X., ZHAO, X. and REN, J. 2019. A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading. Complexity [online], 2019: complex deep learning and evolutionary computing models in computer vision, article ID 8641074. Available from: https://doi.org/10.1155/2019/8641074

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in... Read More about A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading..

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. (2019)
Journal Article
PADFIELD, N., ZABALZA, J., ZHAO, H., MASERO, V. and REN, J. 2019. EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors [online], 19(6), article 1423. Available from: https://doi.org/10.3390/s19061423

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a s... Read More about EEG-based brain-computer interfaces using motor-imagery: techniques and challenges..

Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images. (2019)
Journal Article
CAO, F., YANG, Z., REN, J., CHEN, W., HAN, G. and SHEN, Y. 2019. Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 57(8), pages 5580-5594. Available from: https://doi.org/10.1109/tgrs.2019.2900509

Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single... Read More about Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images..

Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study. (2019)
Journal Article
ZABALZA, J., FEI, Z., WONG, C., YAN, Y., MINEO, C., YANG, E., RODDEN, T., MEHNEN, J., PHAM, Q.-C. and REN, J. 2019. Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study. Sensors [online], 19(6), article 1354. Available from: https://doi.org/10.3390/s19061354

Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a c... Read More about Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study..

Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions. (2019)
Journal Article
XU, X., LI, G., XIE, G., REN, J. and XIE, X. 2019. Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions. Complexity [online], 2019: complex deep learning and evolutionary computing models in computer vision, article 9180391. Available from: https://doi.org/10.1155/2019/9180391

The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pi... Read More about Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions..

Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images. (2019)
Journal Article
SUN, H., REN, J., ZHAO, H., YAN, Y., ZABALZA, J. and MARSHALL, S. 2019. Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images. Remote sensing [online], 11(5), article 536. Available from: https://doi.org/10.3390/rs11050536

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel le... Read More about Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images..

Coherent narrow-band light source for miniature endoscopes. (2018)
Journal Article
CHEN, Z.-Y., GOGOI, A., LEE, S.-Y., TSAI-LIN, Y., YI, P.-W.Y., LU, M.-K., HSIEH, C.-C., REN, J., MARSHALL, S. and KAO, F.-J. 2019. Coherent narrow-band light source for miniature endoscopes. IEEE journal of selected topics in quantum electronics [online], 25(1), article 7100707. Available from: https://doi.org/10.1109/JSTQE.2018.2836959

In this work, we report the successful implementation of a coherent narrow-band light source for miniature endoscopy applications. An RGB laser module that provides much higher luminosity than traditional incoherent white light sources is used for il... Read More about Coherent narrow-band light source for miniature endoscopes..

Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images. (2018)
Journal Article
CAO, F., YANG, Z., REN, J., LING, W.-K., ZHAO, H., SUN, M. and BENEDIKTSSON, J.A. 2018. Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 56(11), pages 6263-6279. Available from: https://doi.org/10.1109/tgrs.2018.2828601

Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main draw... Read More about Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images..