Efficient Multisource Remote Sensing Image Matching Using Dominant Orientation of Gradient

Image matching is the key step for image registration. Due to the existing nonlinear intensity differences between multisource images, their matching is still a challenging task. A fast matching approach based on dominant orientation of gradient (DOG) is proposed in this article, which is robust to nonlinear intensity variations. The DOG feature maps are constructed by extracting DOG feature of each pixel in the images in the first place. A template matching method is used to determine correspondences between images based on the feature representations. We define a similarity measurement, referred to as sum of cosine differences, which can be accelerated by fast Fourier transform. Subsequently, the subpixel accuracy can be achieved by fitting the similarity measurement using a quadratic polynomial modal. A new variable template matching (VTM) method has been developed to improve the matching performance. Experimental results confirm that the proposed matching approach is robust to nonlinear intensity differences and has time efficiency. The VTM method additionally improves the matching precision effectively.

Automated summary

This article proposes a fast matching approach based on dominant orientation of gradient (DOG) for robust image registration in the presence of nonlinear intensity variations. The method constructs DOG feature maps and utilizes template matching with sum of cosine differences similarity measurement to determine correspondences between images. Additionally, a variable template matching (VTM) method is developed to improve matching precision and performance.