针对光学遥感图像场景分类存在类别变化、样本数量变化，场景图像中背景与重要物体变换大、尺度变化多的问题，提出基于神经网络注意力架构搜索的光学遥感图像场景分类方法，由算法自适应在神经网络中搜索卷积、池化、注意力等操作，构建能完成光学遥感图像场景分类任务的神经网络。为保证搜索神经网络过程稳定性，提出两段式贪婪策略网络搜索方法，分阶段丢弃无用操作，减少搜索算法负担、提高搜索速度。最后为了关注各物体与场景关联信息，提出自上而下的网络连接策略，充分复用各阶段多尺度特征图的语义。实验结果证明：该方法相较于手工设计的经典深度学习方法具有更好的性能。在AID、NWPU、PATTERNET 3个遥感图像标准数据集上总体精度均超过经典方法。在AID数据集上准确率达到94.04%；在PATTERNET数据集上准确率达到99.62%；在NWPU数据集上达到95.49%。

With majority problems in image scene of optical remote， changing category in classification， variational size in sample， diverse changing of scale between backgrounds and essential objectives， for instance， new Classification Algorithm for scene classification of optical remote sensing image base on attention architecture search of neural network is proposed in this paper. This algorithm can search convolution， pooling， attention and other operations in the neural network， adaptively； and complete the construction task of scene classification for optical remote sensing images in neural network. Two-stage greedy algorithms network search is mentioned in order to ensure the stability of neural search network. This method abandons useless operations in stage which can reduce algorithm burden and improve speed of search. Furthermore， a top-bottom connection strategy of network， which can fully reuse the semantics of multi-scale feature maps in each stage， is proposed to merge information between each object and scene. The experimental results proved that the method proposed in this paper has better performance than the classical deep learning method designed by hand. Overall， the accuracy of this method in all three remote sensing image-standard data sets （AID， NWPU and PatterNet） is exceeding the classic method. The accuracy rate of AID data set， PatterNet data set， and NWPU data set are 94.04%， 99.62%， and 95.49%， respectively.