Domain Adaptation Techniques for Harmonizing Multi-Source Aerial Imagery: A Case Study on the Netherlands Landscape Data

Abstract

This research presents a novel application of deterministic neural color style transfer techniques to address color inconsistencies in large-scale aerial imagery datasets. The study tackles the significant challenge of processing a 29GB, 10×10km aerial dataset at 8cm spatial resolution, where color variations arise from atmospheric conditions, sensor calibration differences, and temporal acquisition factors. The methodology employs a deterministic neural color mapping (DNCM) framework that ensures mathematically consistent pixel-level transformations, eliminating the artifacts common in conventional CNN-based approaches. A key innovation is the implementation of a self-supervised learning strategy using approximately 300 professional Look-Up Tables (LUTs) to train neural networks for separating geometric content from color characteristics without requiring paired training data. The most significant breakthrough is the model's ability to apply arbitrary color styles without requiring retraining—a capability that enables instant adaptation to different atmospheric conditions and imaging scenarios. This represents a fundamental advancement over existing approaches that require weeks of retraining for each new style application. The implementation utilizes an optimized tile-based processing pipeline with region-adaptive content matrix pre-computation to address tile boundary consistency issues. The system successfully processes the complete dataset in 1 hour and 16 minutes while maintaining full spatial resolution and achieving seamless color harmonization across three distinct geographic regions. Comparative evaluation demonstrates substantial advantages over alternative methods: superior quality compared to histogram matching (which produces artifacts and unnatural color mappings) and elimination of the massive computational requirements that make CycleGAN approaches impractical (requiring one week of training with inferior results). The research contributes practical tools for operational remote sensing workflows, enabling automated color harmonization in multi-source remote sensing applications and supporting improved visual interpretation and automated analysis of aerial imagery across diverse applications. The deterministic approach shows particular promise for cross-platform harmonization between satellite and aerial imagery systems, where consistent color characteristics are essential for reliable analysis and interpretation.RetryClaude can make mistakes. Please double-check responses.