Variational level set segmentation for forest based on MCMC sampling

Abstract

Environmental protection is one of the themes of today's world. The forest is a recycler of carbon dioxide and natural oxygen bar. Protection of forests, monitoring of forest growth is long-term task of environmental protection. It is very important to automatically statistic the forest coverage rate using optical remote sensing images and the computer, by which we can timely understand the status of the forest of an area, and can be freed from tedious manual statistics. Towards the problem of computational complexity of the global optimization using convexification, this paper proposes a level set segmentation method based on Markov chain Monte Carlo (MCMC) sampling and applies it to forest segmentation in remote sensing images. The presented method needs not to do any convexity transformation for the energy functional of the goal, and uses MCMC sampling method with global optimization capability instead. The possible local minima occurring by using gradient descent method is also avoided. There are three major contributions in the paper. Firstly, by using MCMC sampling, the convexity of the energy functional is no longer necessary and global optimization can still be achieved. Secondly, taking advantage of the data (texture) and knowledge (a priori color) to guide the construction of Markov chain, the convergence rate of Markov chains is improved significantly. Finally, the level set segmentation method by integrating a priori color and texture for forest is proposed. The experiments show that our method can efficiently and accurately segment forest in remote sensing images.