Activation Regression for Continuous Domain Generalization with Applications to Crop Classification

Samar Khanna; Bram Wallace; Kavita Bala; Bharath Hariharan

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arXiv · 2204.07030

Activation Regression for Continuous Domain Generalization with Applications to Crop Classification

Samar Khanna·Bram Wallace·Kavita Bala·Bharath Hariharan

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Abstract

Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem, demonstrating how models generalise better with appropriate domain knowledge. We develop a dataset spatially distributed across the entire continental United States, providing macroscopic insight into the effects of geography on crop classification in multi-spectral and temporally distributed satellite imagery. Our method demonstrates improved generalisability from 1) passing geographically correlated climate variables along with the satellite data to a Transformer model and 2) regressing on the model features to reconstruct these domain variables. Combined, we provide a novel perspective on geographic generalisation in satellite imagery and a simple-yet-effective approach to leverage domain knowledge. Code is available at: \url{https://github.com/samar-khanna/cropmap}

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Samar Khanna, Bram Wallace, Kavita Bala, Bharath Hariharan. 2022-04-14. Activation Regression for Continuous Domain Generalization with Applications to Crop Classification. https://arxiv.org/abs/2204.07030

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