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.
翻译:在本文中,我们提出了一套可靠和低复杂程度的遥感图像分类深层学习模型(RSIC),这是确定遥感图像现场的任务,特别是,我们首先评估不同的低复杂程度和基准深神经网络:移动NetV1、移动NetV2、NASNetMobile和高效NetB0,这些网络展示了低于500万(M)的可培训参数的数量。在指出最佳网络结构之后,我们通过对从网络中层提取的多个地貌图进行关注计划,进一步改进网络的性能。为了利用关注计划处理增加模型足迹的问题,我们采用了量化技术,以满足20个MB记忆占用的最大值。我们通过对基准数据集NWPU-RESISC45进行广泛的实验,我们实现了一个强健和低兼容性模型,该模型对最先进的系统具有竞争力,而且对边缘装置的实际应用潜力非常有竞争力。