A Geometric Primitive Extraction Process for Remote Sensing Problems

摘要

This paper presents a new approach to describe images in terms of geometric objects. Methods based on conventional stochastic marked point processes have already led to convincing image analysis results but possess several drawbacks such as complex parameter tuning, large computing time, and lack of generality. We propose a generalized marked point process model which can be performed in shorter computing times and applied to a variety of applications without modifying the model or tuning parameters. In our approach, both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model. A Jump-Diffusion process is performed to find the optimal object configuration. Experiments with remotely sensed images show good potentialities of the proposed approach.