Computational Intelligence in Remote Sensing Image Registration: A survey

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►This paper mainly discusses the applications of computational intelligence in remote sensing image registration, which mainly includes evolutionary algorithms and deep learning. The first author is associate professor Wu Yue of Xidian University, and the correspondence author is Professor Gong Maoguo of Xidian University.

 

 

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. In the practical application of remote sensing images, registration is an indispensable step in the process of image fusion, change detection, feature recognition, image mosaics, image segmentation and image classification. Many later theories and applications are carried out on the assumption that the registration problem has been solved. The accuracy of remote sensing image registration will directly affect the accuracy and application effects of the final application results. 

 

In recent years, with the continuous improvement of human ability in observing the earth, the registration of remote sensing images has made great progress. The purpose of image registration is trying to match two or more images which are about the same object but taken in different situations, such as with different time, different viewpoints and different sensors. Compared with natural image registration, remote sensing image registration is more challenging because remote sensing images have more complex features. The procedure of remote sensing image registration can be divided into five steps, which are preprocessing, feature selection, feature correspondence, determining transformation functions, and resampling. In this paper, we mainly discuss the applications of computational intelligence in remote sensing image registration, which mainly includes evolutionary algorithms and deep learning.

 

Evolutionary algorithms have been widely used in remote sensing image registration, and have achieved great performance. An evolutionary algorithm is a heuristic global optimization probability search algorithm, of which the principle is to simulate the evolution of the survival of the fittest in the ecosystem. Compared with traditional optimization algorithms, evolutionary algorithms have the following advantages:

 

1) Evolutionary algorithms do not require the strict definitions of mathematical models to solve problems. When the actual application requirements are abstracted as an optimization problem, the evolutionary algorithm could make full use of the corresponding fitness function to find the approximate optimal solution without relying on other information, and the actual application requirements could be satisfied.

2) In the optimization process, the traditional optimization strategy is to start from an initial value and then iterate step by step to find the optimal solution, but the evolutionary algorithm starts from a population that contains many points, and finds the optimal or suboptimal solution through the evolution iteration of the population.

3) The robustness and embedded parallelism of the evolutionary algorithm makes the algorithm very effective in finding the global optimal solution, and it is not easy to fall into the dilemma of the local optimal solution, and realize the global optimization in the probability sense.

 

Because the task of remote sensing image registration could be regarded as an optimization problem by establishing a reasonable model and evolutionary algorithms have great performance in optimization problems, evolutionary algorithm has important applications in solving the task of remote sensing image registration.

 

Due to the powerful learning ability of deep learning, it has been widely used in computer vision tasks, such as image registration, object detection, change detection and image fusion. Especially with the introduction of a series of outstanding feature extraction networks, such as AlexNet, VGGNet, and GoogleNet, remote sensing image registration based on deep learning has achieved satisfactory performance. The convolutional neural networks (CNNs) simulate the connection pattern between neurons in animal visual cortical tissue, which are a deep learning structure of a multilayer feed-forward artificial neural network. CNNs are generally composed of multiple convolutional layers, pooling layers and fully connected layers connected to each other. The convolution layer uses various convolution kernels to perform convolution operations on the input to extract various features. The pooling layer performs dimensionality reduction on the input through pooling operations, thereby reducing the number of network parameters. The fully connected layer is usually the last part of CNNs and is a traditional multilayer perceptron network. Each neuron is connected to each neuron of the previous layer. The powerful feature extraction and representation capabilities of CNNs can overcome the instability of low-level features and improve the reliability of registration.

 

Compared with natural images, remote sensing images could be obtained in various ways, the image content is difficult to understand, and their features are not obvious. The method of manually designed features has a narrow scope of application in remote sensing images. It is impossible to predict uncontrollable changes in remote sensing images, and it is difficult to extract discriminative features. For the traditional method, there is no feedback of information between feature extraction and feature matching. 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.

 

Computational intelligence is intelligent, parallel and robust, and does not depend on the characteristics of the problem itself. The computational intelligence-based algorithms are basically optimized for solving problems in a group collaboration manner, which is very suitable for large-scale parallel processing. Algorithms based on computational intelligence have good fault tolerance and are insensitive to initial conditions. It can find optimal solutions under different conditions.

 

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Computational Intelligence in Remote Sensing Image Registration: A survey

Yue Wu, Jun-Wei Liu, Chen-Zhuo Zhu, Zhuang-Fei Bai, Qi-Guang Miao, Wen-Ping Ma, Mao-Guo Gong

http://www.ijac.net/en/article/doi/10.1007/s11633-020-1248-x 

https://link.springer.com/article/10.1007/s11633-020-1248-x 

 

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