A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints

Abstract

In this paper, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods.

A Mismatch Removal Method Based on Global Constraint and Local Geometry Preservation for Lunar Orbiter Images

“…Firstly, most imaging sensors on orbiters are linear-array push broom cameras which are characterized by multicentric imaging [21]. In this case, linear-array images suffer from local deformations due to the rugged terrain relief or imaging viewpoint variations [22], [23], which limits the performance of the global methods based on robust estimation [24], [25]. Secondly, radiometric differences exist between different lunar orbiter images due to illumination variations [26].…”

“…The issue of repeated textures restricts the extraction of unique local structures, and increases the number of mismatches in the initial matching. This also affects the performance of local constraint-based mismatch removal methods [23]. Hence it is still challenging for efficient orbiter image mismatch removal.…”

CoLM2S: Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

“…Therefore, self-supervised graph representation learnings [1,2,[14][15][16], 1 which aims at extracting node embedding without supervised signals (e.g., node labels), have attracted increasing attention in the community. With the self-supervised signals [17,18] of the original graph network to perform the graph representation learning allow the re-usability of the obtained node embedding for various graph analysis tasks, such as node clustering [19] and graph visualisation [11]. Therefore, how to make full use of the graph network information itself without the external supervised labels to achieve high-quality node embeddings motivates this study.…”

An Adaptive Surrogate-Assisted Endmember Extraction Framework Based on Intelligent Optimization Algorithms for Hyperspectral Remote Sensing Images

“…However, limited to the low spatial resolution of the hyperspectral remote sensor, mixed pixels are inevitably appear in the hyperspectral image. The mixed pixels contain at least one ground object material, such as water, soil, and trees, etc., which interferes with the accurate analysis of the hyperspectral image to a certain extent [5,6]. Spectral unmixing, as an efficient technique to solve the problem of mixed pixels, aims to decompose the mixed pixels into a set of pure substances (also known as endmembers) and estimate the proportion of the corresponding endmembers (also called abundances) [7].…”