Aerial images play a vital role in urban planning and environmental preservation, as they consist of various structures, representing different types of buildings, forests, mountains, and unoccupied lands. Due to its heterogeneous nature, developing robust models for scene classification remains a challenge. In this study, we conduct a literature review of various machine learning methods for aerial image classification. Our survey covers a range of approaches from handcrafted features (e.g., SIFT, LBP) to traditional CNNs (e.g., VGG, GoogLeNet), and advanced deep hybrid networks. In this connection, we have also designed Aerial-Y-Net, a spatial attention-enhanced CNN with multi-scale feature fusion mechanism, which acts as an attention-based model and helps us to better understand the complexities of aerial images. Evaluated on the AID dataset, our model achieves 91.72% accuracy, outperforming several baseline architectures.

翻译：航空影像在城市规划与环境保护中发挥着至关重要的作用，其包含多种结构，代表了不同类型的建筑物、森林、山脉以及未利用土地。由于其异质性，开发用于场景分类的鲁棒模型仍具挑战。本研究对用于航空影像分类的各种机器学习方法进行了文献综述。我们的综述涵盖了从手工特征（如SIFT、LBP）到传统CNN（如VGG、GoogLeNet）以及先进的深度混合网络等一系列方法。在此基础上，我们还设计了Aerial-Y-Net——一种具有多尺度特征融合机制的空间注意力增强CNN，该模型作为基于注意力的架构，有助于我们更好地理解航空影像的复杂性。在AID数据集上的评估表明，我们的模型取得了91.72%的准确率，优于多个基线架构。