Dictionary Learning-Based Hough Transform for Road Detection in Multispectral Image

It is of great importance to determine the location and orientation of a straight road in multispectral images for remote sensing. One of the classical methods for straight line detection is the Hough transform that is widely used in binary images. Although there are many previous works for straight road detection, it is still in its infancy to extract a straight road in multispectral images for remote sensing. In this letter, we propose a multiview dictionary learning formulation to approximate the Hough transform for straight road detection in multispectral images. Our formulation can exploit the complementary among the multiple spectral channels. Furthermore, it is natural to incorporate regularizations of prior information to significantly leverage the performance. We consider $L_{1}$ -norm regularization as a case study and conduct extensive experiments on RSSCN7 data set to verify the proposed algorithm. The experimental results demonstrate the superiority of our method in comparison with traditional methods.