mlsanityData to 7 Oct 2026

A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification

arXiv 2211.02820 ↗First posted 5 Nov 2022cs.CV cs.LG eess.IVcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 70 references that are in the map. First posted on arXiv 5 Nov 2022.

Builds on

20 of 70 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023A Light-weight Deep Learning Model for Remote Sensing Image Classificationcited by 4
  2. 2023Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Networkcited by 3
  3. 2025SEMT: Static-Expansion-Mesh Transformer Network Architecture for Remote Sensing Image Captioningcited by 0