  While image registration has been studied in remote sensing community for
decades, registering multimodal data [e.g., optical, LiDAR, SAR, and map]
remains a challenging problem because of significant nonlinear intensity
differences between such data. To address this problem, this paper presents a
fast and robust matching framework integrating local descriptors for multimodal
registration. In the proposed framework, a local descriptor, such as Histogram
of Oriented Gradient (HOG), Local Self Similarity (LSS), or Speeded-Up Robust
Feature (SURF), is first extracted at each pixel to form a pixel-wise feature
representation of an image. Then we define a similarity measure based on the
feature representation in frequency domain using the 3 Dimensional Fast Fourier
Transform (3DFFT) technique, followed by a template matching scheme to detect
control points between images. In this procedure, we also propose a novel
pixel-wise feature representation using orientated gradients of images, which
is named channel features of orientated gradients (CFOG). This novel feature is
an extension of the pixel-wise HOG descriptors, and outperforms that both in
matching performance and computational efficiency. The major advantage of the
proposed framework includes: (1) structural similarity representation using the
pixel-wise feature description and (2) high computational efficiency due to the
use of 3DFFT. Experimental results on different types of multimodal images show
the superior matching performance of the proposed framework than the
state-of-the-art methods.The proposed matching framework have been used in the
software products of a Chinese listed company. The matlab code is available in
this manuscript.
