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

• URL: http://arxiv.org/abs/2302.13028v1
• Date: Sat, 25 Feb 2023 09:02:01 GMT
• Title: A Light-weight Deep Learning Model for Remote Sensing Image Classification
• Authors: Lam Pham, Cam Le, Dat Ngo, Anh Nguyen, Jasmin Lampert, Alexander Schindler, Ian McLoughlin
• Abstract summary: We present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC) By conducting extensive experiments on the NWPU-RESISC45 benchmark, our proposed teacher-student models outperforms the state-of-the-art systems.
• Score: 70.66164876551674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 valuate 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-student models outperforms the state-of-the-art systems, and has potential to be applied on a wide rage of edge devices.

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