Mr. Hongchao Ma Profile

Hongchao Ma

at Wuhan Univ

SPIE Involvement:

Author

SPIE Journal Paper | 14 February 2018

Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

Hongchao Ma, Zhan Cai, Liang Zhang

JARS, Vol. 12, Issue 01, 016021, (February 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.016021

KEYWORDS: Clouds, Optical filters, Machine learning, Digital filtering, Image filtering, Data modeling, LIDAR, Error analysis, Statistical modeling, Vegetation

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Proceedings Article | 29 December 2008 Paper

Processing of remote sensing information in cooperative intelligent grid environment

Jie Sun, Hongchao Ma, Liang Zhong

Proceedings Volume 7285, 728518 (2008) https://doi.org/10.1117/12.815737

KEYWORDS: Remote sensing, Copper indium gallium selenide, Image fusion, Data processing, Environmental sensing, Process modeling, Spatial resolution, Image segmentation, Sensing systems, Image processing

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Proceedings Article | 11 November 2008 Paper

The design and implementation of a remote sensing image processing system based on grid middleware

Liang Zhong, Hongchao Ma, Honggen Xu, Yi Ding

Proceedings Volume 7146, 71462C (2008) https://doi.org/10.1117/12.813178

KEYWORDS: Image processing, Remote sensing, Computing systems, Web services, Data processing, Sensing systems, Databases, Image compression, Internet, Local area networks

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Proceedings Article | 15 November 2007 Paper

Image registration method based on evolutionary modeling with constraints

Zongyue Wang, Hongchao Ma, Jianwei Zhang, Zhangcan Huang, Jianwei Wu, Honggen Xu

Proceedings Volume 6786, 678630 (2007) https://doi.org/10.1117/12.749751

KEYWORDS: Image registration, Mathematical modeling, Computer programming, Image processing, Evolutionary algorithms, Process modeling, Mining, Synthetic aperture radar, Digital imaging, Radar

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Proceedings Article | 28 October 2006 Paper

Automated targets detection based on level set evolution using radar and optical imagery

Yun Yang, Hongchao Ma, Yan Song

Proceedings Volume 6419, 641913 (2006) https://doi.org/10.1117/12.713013

KEYWORDS: Target detection, Bridges, Image segmentation, Radar, Detection and tracking algorithms, Binary data, Satellite imaging, Earth observing sensors, Satellites, Feature extraction

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Proceedings Article | 28 October 2006 Paper

Application of high spatial resolution airborne hyperspectral remote sensing data in thematic information extraction

Hong-gen Xu, Hong-chao Ma, De-ren Li, Yan Song

Proceedings Volume 6419, 64190H (2006) https://doi.org/10.1117/12.712738

KEYWORDS: Remote sensing, Neural networks, Roads, Reflectivity, Soil science, Image classification, Spatial resolution, Spectral resolution, Databases, Neurons

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Proceedings Article | 4 November 2005 Paper

Automatic ports detection and recognition based on multi-features from satellite imagery

Hongchao Ma, Yun Yang

Proceedings Volume 6044, 60441W (2005) https://doi.org/10.1117/12.655265

KEYWORDS: Image segmentation, Image fusion, Synthetic aperture radar, Detection and tracking algorithms, Image sensors, Avalanche photodetectors, Satellites, Feature extraction, Earth observing sensors, Image resolution

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This paper presents a method for ports detection based on the framework of feature level fusion. Bearing in mind the fact that parallel lines and rectangular corners are main features in most ports, and ports are large scale man-made objects, these features are firstly extracted from high-to-moderate resolution optical satellite imagery. Taking account for the balance of data acquisition and spatial resolution, SPOT panchromatic image is used for such feature extraction. Considering the whether conditions in coastal area, which is characterized by rainy and cloudy climate, Radarsat image with the similar spatial resolution as SPOT panchromatic is used to extract linear features along coastal line. Since ships and boats are typical objects that can be easily detected in radar image, these are considered to be supplemented features for ports detection. All extracted features are associated under the framework of feature level fusion. The whole procedure can be described as follows: the first step is preprocessing the input images, mainly histogram stretching to SPOT image for visual quality improvement and filtering to radar image for denoising speckles. Then registration between SPOT and Radarsat image is carried out. Since Radarsat image is used mainly for coastal line extraction and ship detection, rigorous geometric processing is omitted since little attention will be paid to land area. Common polynomial model is used for co-registration with Ground Control Points manually selected from both images. Due to feature level fusion method is adopted, registration accuracy is not as a key factor as in pixel level fusion. The next step will be linear features and rectangular corners detection both in optical and radar image. The detected linear features are then fitted by least mean-square-error algorithm. All the detected features are associated by simply weighted mean algorithm, with different weights to features from optical and radar images. An automatic ports detection system based on the abovementioned procedure is developed. Experiments show that most ports can be detected by our method.

Proceedings Article | 20 September 2001 Paper

Image comparison based on semivariogram and its application

Hongchao Ma, Deren Li, Shuying Jin, Xiaodong Zhang

Proceedings Volume 4552, (2001) https://doi.org/10.1117/12.441499

KEYWORDS: Image retrieval, Image processing, Image compression, Content based image retrieval, Image analysis, Statistical analysis, Remote sensing, Data analysis, Statistical modeling, Signal processing

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Showing 5 of 8 publications

SPIE Conference Volume | 31 October 2005

MIPPR 2005: Image Analysis Techniques

Conference Committee Involvement (1)

MIPPR 2005: Image Analysis Techniques

31 October 2005 | Wuhan, China

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