# Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models

Source: [Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models](<https://www.sota2.com/research/paper/68fa78351e209131cea77366>)

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

Data updated: 2026-08-05T05:00:05.795Z

Authors: Thanh-Dung Le, Vu Nguyen Ha, Ti Ti Nguyen, Duc-Dung Tran, Hung Nguyen-Kha, Luis M. Garces-Socarras, Juan Carlos Merlano-Duncan, Symeon Chatzinotas

Published: 2024-09-05T20:21:49.000Z

## Abstract

Remote sensing (RS) image classification is central to Earth observation, but onboard deployment requires models that are accurate, efficient, and robust to sensor and transmission degradation. Following a train-on-ground, infer-onboard workflow, we evaluate 14 backbones, including CNNs, ResNets, compact Transformers trained from scratch, and pre-trained Vision Transformers, on EuroSAT and PatternNet. We assess clean-data performance, computational cost, power consumption, and robustness to Gaussian noise, motion blur, and an end-to-end DVB-S2(X) transmission chain with channel impairments and JPEG compression. Pre-trained Vision Transformers generally outperform models trained from scratch while providing better efficiency and corruption resilience. MobileViTV2 achieves the highest clean EuroSAT accuracy at 99.09%, whereas EfficientViT-M2 provides the strongest overall trade-off. It attains 98.76% accuracy, precision, and recall on EuroSAT and 99.52% accuracy on PatternNet, with 203.53 MFLOPs, a 38.19 MB footprint, and the best overall robustness score of 0.79. It also degrades most gracefully under transmission loss and consumes 63.35% less power than MobileViTV2 and 73.33% less than Swin Transformer. These results identify EfficientViT-M2 as a strong backbone for reliable, energy-efficient onboard RS image classification. Code for data augmentation, corruption generation, training, and inference is publicly available.

## 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/2409.03901>)

[PDF](<https://arxiv.org/pdf/2409.03901.pdf>)