  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.
