Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this paper, we propose a learning-based road network extraction framework via a Multi-supervised Generative Adversarial Network (MsGAN), which is jointly trained by the spectral and topology features of the road network. Such a design makes the network capable of learning how to “guess” the aberrant road cases, which is caused by occlusion and shadow, based on the relationship between the road region and centerline; thus, it is able to provide a road network with integrated topology. Additionally, we also present a sample quality measurement to efficiently generate a large number of training samples with a little human interaction. Through the experiments on images from various satellites and the comprehensive comparisons to state-of-the-art approaches on the public datasets, it is demonstrated that the proposed method is able to provide high-quality results, especially for the completeness of the road network.

No abstract

No abstract

No abstract

Image smoothing prefers a good metric to identify dominant structures from textures adaptive of intensity contrast. In this paper, we drop on a novel directional anisotropic structure measurement (DASM) toward adaptive image smoothing. With observations on psychological perception regarding anisotropy, non-periodicity and local directionality, DASM can well characterize structures and textures independent on their contrast scales. By using such measurement as constraint, we design a guided adaptive image smoothing scheme by improving extrema localization and envelopes construction in a structure-aware manner. Our approach can well suppresses the staircase-like artifacts and blur of structures that appear in previous methods, which better suits structure-preserving image smoothing task. The algorithm is performed on a space-filling curve as the reduced domain, so it is very fast and much easy to implement in practice. We make comprehensive comparisons with previous state-of-the-art methods for a variety of applications. Experimental results demonstrate the merit using our DASM as metric to identify structures, and the effectiveness and efficiency of our adaptive image smoothing approach to produce commendable results.