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

Source: [A Light-weight Deep Learning Model for Remote Sensing Image Classification](<https://www.sota2.com/research/paper/68fa6ead1e209131cea415af>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:03:50.423Z

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

Published: 2023-02-25T09:02:01.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2302.13028>)

[PDF](<https://arxiv.org/pdf/2302.13028.pdf>)