Abstract
In recent years, computational intelligence has been widely used in many fields and achieved remarkable performance. Evolutionary computing and deep learning are important branches of computational intelligence. Many methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image registration. This paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep learning. In the part of remote sensing image registration based on evolutionary calculation, the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is discussed. The application of deep learning in remote sensing image registration is also discussed. At the same time, the development status and future of remote sensing image registration are summarized and their prospects are examined.
Highlights
Remote sensing image registration has important applications in remote sensing image processing, and is a basic problem in many remote sensing information extraction and processing technologies
Wu. et al / Computational Intelligence in Remote Sensing Image Registration: A survey parts: remote sensing image registration based on evolutionary algorithms[52−63] and remote sensing image registration based on swarm intelligence algorithms[64−70]
Ma et al.[109] proposed a new two-step registration method which is based on deep and local features regions, this method includes a matching method based on convolutional neural networks (CNNs) features and a spatial relationship remote sensing image registration method based on a point matching strategy
Summary
Remote sensing image registration has important applications in remote sensing image processing, and is a basic problem in many remote sensing information extraction and processing technologies. Evolutionary algorithms have been widely used in remote sensing image registration, and have achieved great performance[17, 18]. With the introduction of a series of outstanding feature extraction networks, such as AlexNet[30], VGGNet[31], and GoogleNet[32], remote sensing image registration based on deep learning has achieved satisfactory performance. Deep neural networks have powerful learning capabilities, which can extract higher-dimensional features for registration. Deep learning further improves the accuracy and robustness of remote sensing image registration[38]. Algorithms based on computational intelligence have good fault tolerance and are insensitive to initial conditions It can find optimal solutions under different conditions. The remote sensing image registration based on deep learning is reported, and Section 4 draws the conclusions
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