Z
Zelang Miao
Researcher at Central South University
Publications - 58
Citations - 1452
Zelang Miao is an academic researcher from Central South University. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 16, co-authored 52 publications receiving 1035 citations. Previous affiliations of Zelang Miao include Hong Kong Polytechnic University & China University of Mining and Technology.
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An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery
TL;DR: An integrated method to extract urban main-road centerlines from satellite optical images using general adaptive neighborhood to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups is presented.
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Confidence Analysis of Standard Deviational Ellipse and Its Extension into Higher Dimensional Euclidean Space
TL;DR: A novel approach to constructing the same SDE based on spectral decomposition of the sample covariance is proposed, by which the SDE concept is naturally generalized into higher dimensional Euclidean space, named standard deviational hyper-ellipsoid (SDHE).
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Road Centerline Extraction From High-Resolution Imagery Based on Shape Features and Multivariate Adaptive Regression Splines
TL;DR: This letter presents a new method to extract the road centerline from high-resolution imagery based on shape features and multivariate adaptive regression splines (MARS), in which potential road segments were obtained based onshape features and spectral feature, followed by MARS to extract road centerlines.
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Landslide mapping from aerial photographs using change detection-based Markov random field
TL;DR: A change detection-based Markov random field (CDMRF) method is proposed for near-automatic LM from aerial orthophotos, which is the first time CDMRF is used to LM from bitemporal aerial photographs.
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A Semi-Automatic Method for Road Centerline Extraction From VHR Images
TL;DR: This letter presents a semi-automatic approach to delineating road networks from very high resolution satellite images and demonstrates that this proposed method can extract smooth correct road centerlines.