Ms. Jinping Zhang Profile

Jinping Zhang

at Hebei Univ

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Proceedings Article | 16 October 2009 Paper

Spatio-temporal contextual approaches to mapping land cover change based on remote sensing imagery

Jingxiong Zhang, Jinping Zhang, Yunwei Tang, Jiong You

Proceedings Volume 7492, 74923Y (2009) https://doi.org/10.1117/12.838544

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This paper explores data-driven methods for quantifying and incorporating spatio-temporal contextual information in the mapping of land cover change. In remote sensing, area classes of land cover are typically mapped via statistical manipulation of feature-space measurement, e.g., reflectance data, and other ancillary data. Contextual information has been known to have the potential of increasing the accuracy of land cover classification and change detection, on the ground that land cover often exhibits spatial and temporal correlations and, as such, should be properly accommodated. In Bayesian methods, a priori probabilities of class occurrences can be considered as contextual information, which are combined with class-conditional probability densities to arrive at discriminant decisions with minimized misclassification. These prior probabilities may be made to vary locally to honor variability in the strengths of spatial dependence in class occurrences. For deriving local prior joint probabilities in land cover co-occurrences over time, a modified Expectation and Maximization (EM) algorithm was developed, in which a local window size can be adjusted in the light of spatial dependences inferred from class probability densities computed from spectral data. Empirical studies were performed using bi-temporal Landsat TM image subsets in Wuhan, which confirmed the comparative benefits of incorporating localized prior probabilities in land cover change detection.

Proceedings Article | 14 November 2007 Paper

Geostatistical framework for conflation of heterogeneous geospatial data

Jinping Zhang, Deren Li, Jingxiong Zhang

Proceedings Volume 6790, 679038 (2007) https://doi.org/10.1117/12.750942

KEYWORDS: Data modeling, Remote sensing, Data processing, Image fusion, Data integration, Geographic information systems, Error analysis, Lithium, Associative arrays, Roads

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The term conflation is used as a superset of all kinds of integral approaches to combining heterogeneous geospatial data, aiming for synergism in geospatial information processes, thus added values in resultant information products. A coherent strategy for conflation is built upon an evaluation of spatial data models, which include discrete objects that are geo-referenced by position and associated with some qualitative and/or quantitative attributes, and fields that are continuous or discrete in terms of the scale of measurement. Regardless of whether positional errors or errors in fields are concerned, they can be conceived of as being realizations of regionalized random variables. Therefore, multivariate geostatistics provides a straightforward framework for conflation of spatial data. Scale is an important metric in spatial data, which can be handled in co-kriging procedures by incorporating block-support variograms derived from point-support variograms, functioning as either downscaling or upscaling depending on the interaction between the existing data and the information or analysis required. The methods for scale-dependent manipulation and cross-scale integration of multi-source data will be described, followed by some discussions.

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