Evolutionary Neural Architecture Search for Remote Sensing Image Classification

Remote sensing scene classification is a vital task in remote sensing image analysis with significant application potential. In recent years, convolutional neural network (CNN)-based methods have shown remarkable promise in classifying remote sensing scene images. However, these methods often require extensive trial and error and rely heavily on expert knowledge. To address these challenges, this article proposes a novel neural architecture search (NAS) approach that automatically designs CNNs for remote sensing scene classification. Specifically, an evolutionary algorithm (EA) is employed to search for well-structured basic modules, which are then combined to construct a new architecture. To further enhance the search process, a new population generation strategy is introduced to promote diversity and mitigate premature convergence. Additionally, a random forest-based selection mechanism is utilized to identify high-quality individuals based on estimated fitness values, effectively reducing computational complexity. The proposed approach is evaluated on three benchmark remote sensing scene datasets and compared with several widely used CNNs. The experimental results demonstrate that the proposed approach can discover CNN architectures that not only surpass state-of-the-art performance but also achieve this with fewer parameters and lower search cost.