All Outputs (34)

Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. (2020)
Journal Article
LUO, F., GUO, T., LIN, Z., REN, J. and ZHOU, X. 2020. Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 4242-4256. Available from: https://doi.org/10.1109/jstars.2020.3011431

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to... Read More about Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image..

STEM teaching for the Internet of Things maker course: a teaching model based on the iterative loop. (2020)
Journal Article
CHEN, R., ZHENG, Y., XU, X., ZHAO, H., REN, J. and TAN, H.-Z. 2020. STEM teaching for the Internet of Things maker course: a teaching model based on the interative loop. Sustainability [online], 12(14), article 5758. Available from: https://doi.org/10.3390/su12145758

As the key technology for 5G applications in the future, the Internet of Things (IoT) is developing rapidly, and the demand for the cultivation of engineering talents in the IoT is also expanding. The rise of maker education has brought new teaching... Read More about STEM teaching for the Internet of Things maker course: a teaching model based on the iterative loop..

A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. (2020)
Journal Article
FU, H., SUN, G., ZABALZA, J., ZHANG, A., REN, J. and JIA, X. 2020. A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 2214-2225. Available from: https://doi.org/10.1109/JSTARS.2020.2992230

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conve... Read More about A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging..

Content-sensitive superpixel generation with boundary adjustment. (2020)
Journal Article
ZHANG, D., XIE, G., REN, J., ZHANG, Z., BAO, W. and XU, X. 2020. Content-sensitive superpixel generation with boundary adjustment. Applied sciences [online], 10(9), article 3150. Available from: https://doi.org/10.3390/app10093150

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy... Read More about Content-sensitive superpixel generation with boundary adjustment..

Spatial residual blocks combined parallel network for hyperspectral image classification. (2020)
Journal Article
ZHANG, B., QING, C., XU, X. and REN, J. 2020. Spatial residual blocks combined parallel network for hyperspectral image classification. IEEE access [online], 8, pages 74513-74524. Available from: https://doi.org/10.1109/ACCESS.2020.2988553

In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI cla... Read More about Spatial residual blocks combined parallel network for hyperspectral image classification..

Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor. (2020)
Journal Article
HASAN, M.J., KIM, J., KIM, C.H. and KIM, J.-M. 2020. Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor. Applied sciences [online], 10(7), article 2525. Available from: https://doi.org/10.3390/app10072525

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designe... Read More about Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor..

Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. (2020)
Journal Article
SUN, H., REN, J., ZHAO, H., SUN, G., LIAO, W., FANG, Z. and ZABALZA, J. 2022. Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. IEEE transactions on cybernetics [online], 52(1), pages 215-227. Available from: https://doi.org/10.1109/TCYB.2020.2977750

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still la... Read More about Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection..

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..

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..

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..

Heterogeneous parallelization for object detection and tracking in UAVs. (2020)
Journal Article
RABAH, M., ROHAN, A., HAGHBAYAN, M.-H., PLOSILA, J. and KIM, S.-H. 2020. Heterogeneous parallelization for object detection and tracking in UAVs. IEEE access [online], 8, pages 42784-42793. Available from: https://doi.org/10.1109/ACCESS.2020.2977120

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bot... Read More about Heterogeneous parallelization for object detection and tracking in UAVs..

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..