Mr. Dengkui Mo Profile

Dengkui Mo

at Central South Univ

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 8 August 2007 Paper

Remotely sensed imagery intelligent interpretation based on image segmentation and support vector machines

Dengkui Mo, Hui Lin, Jiping Li, Hua Sun, Tailong Liu, Yujiu Xiong

Proceedings Volume 6752, 67520N (2007) https://doi.org/10.1117/12.760450

KEYWORDS: Image segmentation, Image classification, Remote sensing, Agriculture, Image processing, Image analysis, Image processing algorithms and systems, Feature extraction, Human vision and color perception, Forestry

Read Abstract +

Proceedings Article | 28 October 2006 Paper

Object-oriented information extraction of forest resources from high resolution remote sensing

Dengkui Mo, Hui Lin, Hua Sun, Jiping Li, Yujiu Xiong, Tailong Liu

Proceedings Volume 6419, 641916 (2006) https://doi.org/10.1117/12.713116

KEYWORDS: Image segmentation, Remote sensing, Image classification, Image analysis, Spatial resolution, Forestry, Image processing algorithms and systems, Geographic information systems, Image processing, Feature extraction

Read Abstract +

Remote sensing is a powerful tool for precision forestry, providing the forestry industry with spatial information on environment impacts, growth and yield, site variables and damage assessment. Nonetheless the extraction of information from remotely sensed imagery is presently labor intensive requiring highly qualified remote sensing experts, making this information source expensive and slow. With the improvement of spatial resolution, very high resolution remote sensing image are now a competitive alternative to aerial photography and field visits in forest resource survey. In recent years, numerous classification methods were described in the literature and they can be classified into two large classes: traditional pixel-based classification and object-oriented image analysis method. Traditional pixel-based classification techniques either supervised methods or unsupervised method all based on spectral analysis of individual pixels and significant progress has been achieved in recent years. However, these approaches have their limitations since the problem of mixed pixels is indeed reduced, but the internal variability and the noise within land cover classes are increased the improved spatial resolution. In order to improve the classification accuracy, object-oriented image analysis concept has been proposed. This paper explores the use of object oriented image analysis approaches in mapping forest resource and introduces a fast and robust segmentation algorithm--mean shift. The study is based on SPOT-5 image covering the national forest park of Tian'eshan, Zixing City, Hunan, China. Image processing included geometric and atmospheric correction and image segmentation and classification using spectral and spatial information to separate 5 classes. 86.5342% overall accuracy was achieved with this approach. In additional, object oriented image analysis method is compared with traditional pixel based method. The results show the importance, capabilities and challenges of object oriented approaches in providing detailed and accurate information about the physical structure of forest areas.

Proceedings Article | 19 May 2006 Paper

Study on the technology of classifying high-resolution remote sensing image based on multi-feature

Hui Lin, Jiping Li, Dengkui Mo, Yujiu Xiong, Hua Sun, Xuiying Liu

Proceedings Volume 6199, 61990H (2006) https://doi.org/10.1117/12.673666

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years