论文标题
遥感图像分类的强大而低的复杂性深度学习模型
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification
论文作者
论文摘要
在本文中，我们为遥感图像分类（RSIC）提出了强大且低的复杂性深度学习模型，即确定遥感图像场景的任务。特别是，我们首先评估不同的低复杂性和基准深神经网络：MobilenetV1，MobilenetV2，NasnetMobile和EfficityNetB0，它们列出了低于500万（m）的可训练参数的数量。在指出了最佳的网络体系结构之后，我们通过将注意力方案应用于网络中层层中提取的多个特征图来进一步提高网络性能。为了处理增加模型足迹作为使用注意方案的问题，我们应用了量化技术来满足20 MB的记忆占用。通过在基准数据集NWPU-Resisc45上进行广泛的实验，我们实现了一个强大且低复杂的模型，该模型对最先进的系统非常有竞争力，并且可以在边缘设备上实现现实生活。
In this paper, we present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the scene of a remote sensing image. In particular, we firstly evaluate different low complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2, NASNetMobile, and EfficientNetB0, which present the number of trainable parameters lower than 5 Million (M). After indicating best network architecture, we further improve the network performance by applying attention schemes to multiple feature maps extracted from middle layers of the network. To deal with the issue of increasing the model footprint as using attention schemes, we apply the quantization technique to satisfy the maximum of 20 MB memory occupation. By conducting extensive experiments on the benchmark datasets NWPU-RESISC45, we achieve a robust and low-complexity model, which is very competitive to the state-of-the-art systems and potential for real-life applications on edge devices.
