Jia Zhige

Modeling Spatial Relationships for Remote Sensing Image Processing Based on Fuzzy Set Theory

Spatial information is a crucial aspect of image processing, computer vision and structural recognition for modeling context as well as resolving the uncertainties caused by the ambiguities in low-level features. This calls for the framework of fuzzy sets, which exhibits nice features to represent spatial imprecision, and which provides powerful tools for fusion, decision-making and reasoning. In this paper, we present that several spatial fuzzy relationships constructed from fuzzy set operators and norms, and suggest some potential applications in structural object recognition and image retrieval based on fuzzy spatial relationships.

Improvement of instantaneous positioning by spatial stacking and modified sidereal filtering methods with application to the 2008 Ms8.0 Wenchuan earthquake

Abstract: We obtained several displacement time series from the Sichuan permanent GPS net and processed the 1-Hz data observed during a few days before the 2008 M s8.0 Wenchuan earthquake by double-difference instantaneous positioning technique. We filtered the data by the spatial stacking and the modified sidereal filtering methods to reduce correlation bias in space and time. The results indicate that these methods can improve the precision significantly.