Dr. Hongzhi Wu Profile

Dr. Hongzhi Wu

at Shandong Institute for Development Strategy of sci

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Proceedings Article | 9 August 2018 Paper

Road information extraction based on knowledge using WorldView-2 images

Hongzhi Wu, Dong Yang, Rui Qiao, Feng Shi

Proceedings Volume 10806, 1080654 (2018) https://doi.org/10.1117/12.2503031

KEYWORDS: Roads, Feature extraction, Image processing, Detection and tracking algorithms

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Road is not only a basic feature of geographic information, but also the most frequently changed feature. Due to rapid development, road information of the map is not consistent with the actual case of land features. Road extraction from digital images is of fundamental importance in effective urban planning and updating GIS databases. There is an urgent need for updating road information in a timely manner. Therefore, a large amount of research is being dedicated on the development of efficient methods to extract the geographic features (such as roads) from digital remote sensed images. This paper applies semi-automatic approach to extract different road types from high-resolution remote sensing images. The approach is based on a K-Nearest Neighbor(KNN)and membership function algorithm(MFA) method. First the outline of the road is detected based on different segmentation scales. Membership function algorithm(MFA)-threshold value method reflecting various spatial, spectral, and texture attributes is to modify and optimize. Then the entire image was classified to form a road image. Finally, the quality of detected roads is evaluated. The results of the accuracy evaluation demonstrate that the proposed road extraction approach can provide high accuracy for extraction of different road types.

Proceedings Article | 29 August 2016 Paper

A study of the potential of using worldview-2 of images for the detection of red attack pine tree

Hongzhi Wu

Proceedings Volume 10033, 100333W (2016) https://doi.org/10.1117/12.2244937

KEYWORDS: Remote sensing, Image classification, Vegetation, Sensors, Visible radiation, Satellites, Spatial resolution, Infrared sensors, Data modeling, Near infrared

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Forest disturbances in South China caused by pine wood nematode may result in widespread tree mortality. In order to decrease damage to forest ecosystem and huge loss to national economy, early detection, early diagnosis to individual infected tree is essential to forest management agencies. However field survey is hard to achieve the fine management requirements. Satellite remote sensing technology has the characteristics of landscape of coverage, convenient, and fast in formation acquisition, so it is one of the most important and most effective means of red attack monitoring. The support vector machine(SVM) classification algorithm have been proposed as an alternative for classification of remote sensing data. The study is based on a multispectral Worldview-2(WV-2) scene and uses support vector machine(SVM) methods. We compared the eight bands with three bands of the image based on SVM and came to the conclusion that WorldView-2 are suitable for individual tree identification. Three visible bands spectral data can also discriminate discolored individual tree successfully. In other words, three visible bands of remote sensing can meet the requirements of red attack pine estimation and extraction.

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