Activation Regression for Continuous Domain Generalization with Applications to Crop Classification

• URL: http://arxiv.org/abs/2204.07030v1
• Date: Thu, 14 Apr 2022 15:41:39 GMT
• Title: Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
• Authors: Samar Khanna, Bram Wallace, Kavita Bala, Bharath Hariharan
• Abstract summary: Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. We model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem. We develop a dataset spatially distributed across the entire continental United States.
• Score: 48.795866501365694
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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