SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search

The scene classification approaches using deep learning have been the subject of much attention for remote sensing imagery. However, most deep learning networks have been constructed with a fixed architecture for natural image processing, and they are difficult to apply directly to remote sensing images, due to the more complex geometric structural features. Thus, there is an urgent need for automatic search for the most suitable neural network architecture from the image data in scene classification, in which a powerful search mechanism is required, and the computational complexity and performance error of the searched network should be balanced for a practical choice. In this article, a framework for scene classification network architecture search based on multi-objective neural evolution (SceneNet) is proposed. In SceneNet, the network architecture coding and searching are achieved using an evolutionary algorithm, which can implement a more flexible hierarchical extraction of the remote sensing image scene information. Moreover, the computational complexity and the performance error of the searched network are balanced by employing the multi-objective optimization method, and the competitive neural architectures are obtained in a Pareto solution set. The effectiveness of SceneNet is demonstrated by experimental comparisons with several deep neural networks designed by human experts.

Automated summary

This study introduces a scene classification network architecture search method based on multi-objective neural evolution, which allows for more flexible extraction of remote sensing image scene information and achieves effective results in balancing computational complexity and performance error.