A Light-Weight Deep Learning Model for Remote Sensing Image Classification

Lam Pham, Cam Le, Dat Ngo, Anh Nguyen, Jasmin Lampert, Alexander Schindler, Ian McLoughlin

Abstract: In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first evaluate various benchmark convolutional neural network (CNN) architectures: MobileNet V1/V2, ResNet 50/151V2, InceptionV3/InceptionResNetV2, EfficientNet B0/B7, DenseNet 121/201, ConNeXt Tiny/Large. Then, the best performing models are selected to train a compact model in a teacher-student arrangement. The knowledge distillation from the teacher aims to achieve high performance with significantly reduced complexity. By conducting extensive experiments on the NWPU-RESISC45 benchmark, our proposed teacher and student models outper-forms the state-of-the-art systems, and has potential to be applied on a wide range of edge devices.