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.

使用深度学习的场景分类方法已经成为遥感影像的主要关注对象。但是，大多数深度学习网络都具有用于自然图像处理的固定体系结构，并且由于更复杂的几何结构特征而难以直接应用于遥感图像。因此，迫切需要从场景分类中的图像数据中自动搜索最合适的神经网络体系结构，其中需要一种强大的搜索机制，并且应该在一定程度上平衡搜索网络的计算复杂度和性能误差。实际的选择。本文提出了一种基于多目标神经进化的场景分类网络架构搜索框架（SceneNet）。在SceneNet中，网络结构的编码和搜索是通过进化算法实现的，可以实现遥感图像场景信息的更灵活的分层提取。此外，通过采用多目标优化方法来平衡搜索网络的计算复杂度和性能误差，并在Pareto解集中获得竞争性神经体系结构。通过与人类专家设计的几种深度神经网络进行实验比较，证明了SceneNet的有效性。通过采用多目标优化方法来平衡搜索网络的计算复杂度和性能误差，并在Pareto解集中获得竞争性神经体系结构。通过与人类专家设计的几种深度神经网络进行实验比较，证明了SceneNet的有效性。通过采用多目标优化方法来平衡搜索网络的计算复杂度和性能误差，并在Pareto解集中获得竞争性神经体系结构。通过与人类专家设计的几种深度神经网络进行实验比较，证明了SceneNet的有效性。