Professor Jinchang Ren's Outputs (6)

Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification. (2017)
Journal Article
CAO, F., YANG, Z., REN, J., LING, W.-K., ZHAO, H. and MARSHALL, S. 2017. Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification. Remote sensing [online], 9(12), article number 1255. Available from: https://doi.org/10.3390/rs9121255

Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constr... Read More about Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification..

Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries. (2017)
Journal Article
NOOR, S.S.M., MICHAEL, K., MARSHALL, S. and REN, J. 2017. Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries. Sensors [online], 17(11), article number 2644. Available from: https://doi.org/10.3390/s17112644

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms... Read More about Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries..

Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. (2017)
Journal Article
CAO, F., YANG, Z., REN, J., JIANG, M. and LING, W.-K. 2017. Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. Sensors [online], 17(11), article number 2603. Available from: https://doi.org/10.3390/s17112603

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because E... Read More about Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images..

Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor. (2017)
Journal Article
SUN, G., HAO, Y., CHEN, X., REN, J., ZHANG, A., HUANG, B., ZHANG, Y. and JIA, X. 2017. Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor. Remote sensing [online], 9(9), article number 899. Available from: https://doi.org/10.3390/rs9090899

The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially hig... Read More about Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor..

Gravitation-based edge detection in hyperspectral images (2017)
Journal Article
SUN, G., ZHANG, A., REN, J., MA, J., WANG, P., ZHANG, Y. and JIA, X. 2017. Gravitation-based edge detection in hyperspectral images. Remote sensing [online], 9(6), article 592. Available from: https://doi.org/10.3390/rs9060592

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties... Read More about Gravitation-based edge detection in hyperspectral images.

Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China. (2017)
Journal Article
TSOU, J.Y., GAO, Y., ZHANG, Y., SUN, G., REN, J. and LI, Y. 2017. Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China. Remote sensing [online], 9(6), article number 529. Available from: https://doi.org/10.3390/rs9060529

In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-u... Read More about Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China..