Xia Liegang

Zhejiang University of Technology

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Zhanfeng Shen

Chinese Academy of Sciences

Jiancheng Luo

Aerospace Information Research Institute

Xiao Hu

Shanxi Medical University

Jiancheng Luo

Aerospace Information Research Institute

Weihong Wang

Northeast Petroleum University

Tianjun Wu

Chang'an University

Weihong Wang

KU Leuven

Yongwei Sheng

University of California, Los Angeles

Changming Zhu

Jiangsu Normal University

Cheng Qiao

Chinese Academy of Sciences

Cooperative Institutions

Chinese Academy of Sciences

Institute of Remote Sensing and Digital Earth

State Key Laboratory of Remote Sensing Science

Aerospace Information Research Institute

University of Chinese Academy of Sciences

Zhejiang University of Technology

Hangzhou Dianzi University

University of California, Los Angeles

Hebei University of Economics and Business

Hunan Normal University

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An Iterative Approach to Object-oriented Classification of Remotely Sensed Image

Wuhan Daxue xuebao. Xinxi kexue ban (2011)

Weihong Wang Xia Liegang Jiancheng Luo Xiao Hu

While spectral analysis of images has yielded satisfactory results,they may not be enough to extract features from high spatial resolution satellite data.A novel iterative classification approach is proposed,which based on object-oriented technologies.This approach makes use of both pixel spectral features and object features through an iterative model to improve the accuracy of classification.Some adaptive techniques are used for each iteration,which are leading class selection,advanced object-based feature calculating,feature selection based on mutual information.The experiment of land cover classification on SPOT5 image and aerial photography shows this approach produces higher classification accuracy than normal object-oriented classification.

Land Cover

Feature (linguistics)

Contextual image classification

Object based

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Analysis on Dynamic Change and Tendency of Land-cover based on Object\|oriented Classification

Yaogan jishu yu yingyong (2014)

Tianjun Wu Xiao Hu Xia Liegang Jiancheng Luo Zhanfeng Shen

Land Cover

Object based

Change analysis

10.11873/j.issn.1004-0323.2014.4.0600

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Land cover automatic classification based on RS-Informatic Tupu

National Remote Sensing Bulletin (2014)

Xia Liegang Jiancheng Luo Weihong Wang Zhanfeng Shen

Land Cover

10.11834/jrs.20143058

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Registration of remote sensing images of large regions using lake center points as ground control points

National Remote Sensing Bulletin (2013)

Zhanfeng Shen Yongwei Sheng Jiancheng Luo Xia Liegang

Center (category theory)

10.11834/jrs.20132245

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The Theory and Calculation of Spatial-spectral Cognition of Remote Sensing

Diqiu Xinxi Kexue Xuebao (2016)

Jiancheng Luo Tianjun Wu Xia Liegang

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Adaptive Water Body Information Extraction Using RS TUPU Computing Model

Acta Geodaetica et Cartographica Sinica (2011)

Xiao Hu Jiancheng Luo Xia Liegang Zhanfeng Shen Changming Zhu

A RS TUPU computing mechanism is designed,considering the advantages of the features of spatial clustered layer and non-linear spectral mapping layer,which aims to approach the accurate regional boundary of thematic area step by step.Meanwhile,extraction parameters in the process are adjusted automatically by adaptive information computing method.Subsequently,the RS TUPU computing model is proposed,which will be applied in water body extraction.The existing valid methods for water body extraction are analyzed first.On this basis,an adaptive water body extraction method using RS TUPU computing model(WERSTP) is proposed,where ETM image is selected as experimental data source.WERSTP combines with the advantages of methods based on index computing and spectral classification,and the experimental results show that this method obtains an effective extraction result to make it achieve the level of accuracy and automation in water body information extraction.

Water body

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Automatic and Precise Image Registration Based on Phase-Correlation Combined with Surface Fitting

Weihong Wang Xia Liegang Jiancheng Luo Xiao Hu

The original method for image registration based on phase-correlation is only valid, if the shift vector is of integer values. So the error could be up to half of a pixel in theory. Since the estimation of the parameters on rotation and scaling is based on the estimation of these shift values, their accuracy is also restricted. In response to this limitation, we improved the method of search the maximum point of the impulse function. Using surface fitting methods like Parabolic Interpolation or Gaussian Function Fitting Method we could get the location of extreme point in sub-pixel level. So the error would be significantly reduced. The result of some registration experiments showed that the algorithm was valid for dealing with shifted, rotated and scaled images. Through the analysis we can see the improved method greatly increased the accuracy of registration without increasing the computational complexity.

Phase correlation

Image registration

Interpolation

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