基于ResNet双注意力机制的遥感图像场景分类

Remote Sensing Image Scene Classification Based on ResNet and Dual Attention Mechanism

针对基于传统机器学习遥感图像场景分类无法快速有效提取图像特征造成分类结果不准确的问题,提出一种基于注意力残差网络的遥感图像场景分类的方法,以残差网络为基准模型,在通道和空间两个维度上建立注意力模块,实验过程中对参数进行合理有效的设置,调整网络层数优化模型,达到对UC Merced Land-Use数据集的有效分类.实验结果表明,与基于卷积神经网络结构的遥感图像场景分类方法相比,该方法达到了98.1%的准确率.

To deal with the inaccurate classification caused by a failure of quick and effective extraction of image features in the remote sensing image scene classification based on existing machine learning methods,we propose a remote sensing image scene classification method based on residual attention network.With the residual network as the benchmark model,attention modules are created in the dimensions of channel and space.For effective classification of the UC Merced Land-Use dataset,parameters are set reasonably and the model that optimizes the number of network layers is fine-tuned.The results show that the accuracy of our method reaches 98.1%compared with that based on the convolution neural network.