联合集成学习与EfficientNet的光学遥感图像场景分类

深度学习能够提高光学遥感图像场景分类的准确率和效率，但光学遥感图像语义丰富，部分场景仍存在易误分类的情况，同时由网络模型规模扩大带来的硬件要求过高、时间成本消耗过大等问题制约着深度学习网络模型的推广应用。为此，提出一种基于轻量化网络模型的光学遥感图像场景分类方法。通过EfficientNet网络提取图像特征，对图像特征进行复合提取以生成语义信息更丰富的新特征，利用多个子分类器构建集成学习模块解析新特征得到预分类结果，集成加权预分类结果以获得最终的分类结果。在AID和NWPU-RESISC45数据集上的实验结果表明，即使只训练20%的数据样本，该方法也能分别达到94.32%和93.36%的准确率，相对D-CNNs、CNN-CapsNet等方法，所提方法对易误分类场景有更好的分类效果，且参数量和浮点运算量大幅减少。

Scene Classification of Optical Remote Sensing Images Joint Ensemble Learning and EfficientNet

Deep learning has improved the accuracy and efficiency of scene classification for optical remote sensing images, but some scenes are easily misclassified due to the rich semantic information in the images.At the same time, the hardware requirements and time overhead are increasing with the size of scaling network models, which restricts the application of deep learning models.To address the problem, a method for classifying the scenes in optical remote sensing images is proposed based on a lightweight network model.The method employs EfficientNet to extract image features, and then new features with richer information are generated from the extracted features.Multiple sub-classifiers are used to construct an ensemble learning module to analyze the new features, and get the pre-classification results.Finally, the pre-classification results are weighted to obtain the final classification results.The experimental results show that even if only 20% of data samples are used for training, the proposed method still exhibits an accuracy of 94.32% on the AID data set and 93.36% on the NWPU-RESISC45 data set.Compared with D-CNNs, CNN-CapsNet and other methods, the proposed method provides better classification performance with the number of parameters and amount of floating operations greatly reduced.