Remote sensing image scene classification methods based on deep learning have been widely studied and discussed. However, most of the network architectures are directly reliant on natural image processing methods and are fixed. A few studies have focused on automatic search mechanisms, but they cannot weigh the interpretation accuracy and the parameter quantity for practical application. As a result, automatic global search methods based on multiobjective evolutionary computation have more advantages. However, in the ranking process, the network individuals with large parameter quantities are easy to eliminate, but a higher accuracy may be obtained after full training. In addition, evolutionary neural architecture search methods often take several days. In this article, in order to solve the above concerns, we propose an efficient multiobjective evolutionary automatic search framework for remote sensing image scene classification deep learning network architectures (E2SCNet). In E2SCNet, eight kinds of lightweight operators are used to build a diversified search space, and the coding connection mode is flexible. In the search process, a large model retention mechanism is implemented through two-step multiobjective modeling and evolutionary search, where one step involves the “parameter quantity and accuracy,” and the other step involves the “parameter quantity and accuracy growth quantity.” Moreover, a super network is constructed to share the weight in the process of individual network evaluation and promote the search speed. The effectiveness of E2SCNet is proven by comparison with several networks designed by human experts and networks obtained by gradient and evolutionary computing-based search methods.

中文翻译：

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E2SCNet：高效多目标进化自动搜索遥感图像场景分类网络架构


基于深度学习的遥感图像场景分类方法得到了广泛的研究和讨论。然而，大多数网络架构直接依赖于自然图像处理方法并且是固定的。一些研究集中在自动搜索机制上，但无法权衡实际应用的解释精度和参数数量。因此，基于多目标进化计算的自动全局搜索方法更具优势。然而，在排序过程中，参数量大的网络个体很容易被淘汰，但经过充分训练后可能会获得较高的准确率。此外，进化神经架构搜索方法通常需要几天的时间。在本文中，为了解决上述问题，我们提出了一种用于遥感图像场景分类深度学习网络架构的高效多目标进化自动搜索框架（E2SCNet）。 E2SCNet中采用8种轻量级算子构建多样化的搜索空间，编码连接方式灵活。在搜索过程中，通过两步多目标建模和进化搜索实现大模型保留机制，其中一步涉及“参数数量和精度”，另一步涉及“参数数量和精度增长量”。此外，还构建了超级网络，共享个体网络评估过程中的权重，提高搜索速度。通过与人类专家设计的几个网络以及基于梯度和进化计算的搜索方法获得的网络的比较，证明了E2SCNet的有效性。