Deep Learning

Adapting ESRGAN for Multiband Sentinel-2 Imagery

Ddebwashis
·Sep 20, 2025·7 min read

What changes when you bring a super-resolution GAN out of natural images into multiband satellite data.

The problem

Sentinel-2 gives you 13 bands at different ground resolutions. Pretrained ESRGANs assume 3-channel RGB inputs — that breaks fast.

What worked

A domain-specific perceptual loss using a feature extractor trained on multiband patches.

This is paragraph 1. Data science work is part craft and part discipline — the best models are simple, well-validated, and easy to explain. In this post we walk through the intuition, the math just enough to be useful, and a clean implementation you can drop into your own pipeline.

This is paragraph 2. Data science work is part craft and part discipline — the best models are simple, well-validated, and easy to explain. In this post we walk through the intuition, the math just enough to be useful, and a clean implementation you can drop into your own pipeline.

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