Abstract
We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.
Highlights
Image registration is an important step for many fields [1], such as change detection, image fusion, and object recognition
To investigate the performance of our method, we have compared it against three image registration methods: genetic algorithm (GA), particle swarm optimization (PSO) [12], and the differential evolution (DE) [30] algorithm
In the GA algorithm, we set M = 0.05, C = 0.8, where M is the probability of mutation and C is the probability of crossover of GA algorithm
Summary
Image registration is an important step for many fields [1], such as change detection, image fusion, and object recognition. Many feature-based methods have been proposed [4, 5] These methods usually need to initially extract salient features, such as point, edge, contour, and region. Those features are matched using similarity measures to establish the geometric correspondence between two images. One of the main advantages of these approaches is that they are efficient and robust to noise, complex geometric distortions, and significant radiometric differences. They will only perform well on the condition that suitable features are extracted and reliable algorithms are used [3]. For some images, where features are not obvious, intensity-based methods perform better than feature-based methods
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