# Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery

arXiv: https://arxiv.org/abs/2104.00564 · HTML: https://mlsanity.com/paper/2104.00564/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 77 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Bidirectional Learning for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/1904.10620/) (2019)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation](https://mlsanity.com/paper/1612.02649/) (2016)
- [Training generative neural networks via Maximum Mean Discrepancy optimization](https://mlsanity.com/paper/1505.03906/) (2015)
- [Unsupervised Cross-Domain Image Generation](https://mlsanity.com/paper/1611.02200/) (2016)
- [Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes](https://mlsanity.com/paper/1707.09465/) (2017)
- [All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation](https://mlsanity.com/paper/1903.12212/) (2019)
- [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks](https://mlsanity.com/paper/1511.06390/) (2015)
- [Deep Visual Domain Adaptation](https://mlsanity.com/paper/2012.14176/) (2020)
- [Self-attention for raw optical Satellite Time Series Classification](https://mlsanity.com/paper/1910.10536/) (2019)
- [Adversarial Feature Augmentation for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1711.08561/) (2017)
- [Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images](https://mlsanity.com/paper/1905.03198/) (2019)
- [Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)](https://mlsanity.com/paper/2004.12880/) (2020)
- [Improving land cover segmentation across satellites using domain adaptation](https://mlsanity.com/paper/1912.05000/) (2019)
- [Dense Fusion Classmate Network for Land Cover Classification](https://mlsanity.com/paper/1911.08169/) (2019)

## Built on by (most cited first, 18 of 18)

- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)
- [Benchmarking and scaling of deep learning models for land cover image classification](https://mlsanity.com/paper/2111.09451/) (2021)
- [Deep Learning for Satellite Image Time Series Analysis: A Review](https://mlsanity.com/paper/2404.03936/) (2024)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin](https://mlsanity.com/paper/2301.00363/) (2023)
- [Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation](https://mlsanity.com/paper/2304.01029/) (2023)
- [Learning De-Biased Representations for Remote-Sensing Imagery](https://mlsanity.com/paper/2410.04546/) (2024)
- [Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering](https://mlsanity.com/paper/2206.11623/) (2022)
- [A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture](https://mlsanity.com/paper/2210.01272/) (2022)
- [Physics-Informed Real NVP for Satellite Power System Fault Detection](https://mlsanity.com/paper/2405.17339/) (2024)
- [Understanding Knowledge Transferability for Transfer Learning: A Survey](https://mlsanity.com/paper/2507.03175/) (2025)
- [FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing](https://mlsanity.com/paper/2504.16433/) (2025)
- [A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning](https://mlsanity.com/paper/2505.01558/) (2025)
- [Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation](https://mlsanity.com/paper/2306.15517/) (2023)
- [A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series](https://mlsanity.com/paper/2407.02861/) (2024)
- [Domain Adaptation for Big Data in Agricultural Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.05972/) (2025)
- [HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer](https://mlsanity.com/paper/2507.08741/) (2025)
- [Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift](https://mlsanity.com/paper/2607.05645/) (2026)

## Abstract (arXiv)

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time consuming solution that poses strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.