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With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations. [ABSTRACT FROM AUTHOR]

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) are obtained by imaging surface materials using a spectral imager. In practical applications, the hyperspectral remote sensing images are inevitably contaminated by noise, leading to errors in image processing results. Gaussian noise, Poisson noise, and stripe noise are common in HSIs. This article presents an HSI denoising model based on the low rank representation of multi-manifold double constructions. Introducing multiple manifolds into the field of hyperspectral image denoising for the first time, comprehensively obtaining complex information on hyperspectral surfaces. We extended the graph-regularized low-rank representation model (GLRR) by using maximum data variance to construct global information. GLRR obtains local information, and the parallel effects of nearest neighbor and maximum variance graphs take into account both global and local consistency in the denoising process. Our model is not only applicable to the case of a single manifold, but also to the case of multiple manifolds, especially in the case of complex ground target images with multiple manifold spectra. Use the LADMAP iterative algorithm to iteratively solve the proposed algorithm. Avoid introducing auxiliary variables to reduce runtime costs and algorithm complexity. Both on simulated and real datasets, the runtime is relatively fast. We experiments on both real and simulated datasets, as well as real datasets, to verify that our method has good denoising performance when Gaussian, Poisson, and stripe noise occur simultaneously. • Multiple manifolds embedded into the field of hyperspectral image denoising is an extension of the GLRR model. • Global and local structures of the original data are maximized to achieve global and local consistency. • Using LADMAP to reduce algorithm complexity and runtime. [ABSTRACT FROM AUTHOR]

Feature matching is a necessary and important step for remote sensing image registration, intended to establish reliable point correspondences between two sets of features. In this paper, we propose a feature registration model based on local relative motion, which combines Gaussian kernel convolution with relative motion (GRM) vector to obtain better results by removing wrong matches and improving the inlier point accuracy. We first establish putative matching based on the similarity between local descriptors. Then, the preliminary hypothetical matching point set is filtered using consistency with nearest neighbors among the inlier points to obtain a more accurate motion vector, and to fit the real motion vector through the Gaussian convolution kernel. Finally, we find the displacement between the fitted motion vector and the matching generated motion vector. And combine the displacement with the optimization model to find the inlier point set. Experimental results show that our GRM method outperforms related work, achieving better matching results. [ABSTRACT FROM AUTHOR]