  Object extraction from remote sensing images has long been an intensive
research topic in the field of surveying and mapping. Most existing methods are
devoted to handling just one type of object and little attention has been paid
to improving the computational efficiency. In recent years, level set evolution
(LSE) has been shown to be very promising for object extraction in the
community of image processing and computer vision because it can handle
topological changes automatically while achieving high accuracy. However, the
application of state-of-the-art LSEs is compromised by laborious parameter
tuning and expensive computation. In this paper, we proposed two fast LSEs for
man-made object extraction from high spatial resolution remote sensing images.
The traditional mean curvature-based regularization term is replaced by a
Gaussian kernel and it is mathematically sound to do that. Thus a larger time
step can be used in the numerical scheme to expedite the proposed LSEs. In
contrast to existing methods, the proposed LSEs are significantly faster. Most
importantly, they involve much fewer parameters while achieving better
performance. The advantages of the proposed LSEs over other state-of-the-art
approaches have been verified by a range of experiments.
