satharmony 0.1.4

Sensor emulation and harmonization for Earth Observation (Sentinel-2 → Landsat MSS).

🛰️ SatHarmony

Sensor emulation and harmonization for Earth Observation.

SatHarmony provides physically-based sensor emulation to create synthetic historical satellite imagery from modern sensors. Currently supports Sentinel-2 → Landsat MSS emulation with realistic degradation modeling.

Installation

pip install satharmony

# With GeoTIFF I/O support
pip install satharmony[io]

Quick Start

import numpy as np
import satharmony

# Load your Sentinel-2 image (C, H, W) and CloudSEN12 labels (H, W)
s2_image = ...      # shape: (13, 512, 512), float32, [0, 1]
s2_labels = ...     # shape: (512, 512), uint8, classes 0-3

# Emulate both together (recommended - guarantees matching dimensions)
mss, mss_labels, scale_factor = satharmony.emulate_s2_with_labels(
    s2_image, s2_labels, seed=42
)
# mss.shape:        (4, 73, 73)
# mss_labels.shape: (73, 73)

# Or emulate separately (must pass scale_factor to labels)
mss, sf = satharmony.emulate_s2(s2_image, seed=42)
mss_labels = satharmony.emulate_labels(s2_labels, scale_factor=sf)

Features

The MSS emulator applies physically-motivated degradations:

Degradation	Description
Spectral	Band selection (B3, B4, B7, B8 → MSS 4 bands)
Spatial	PSF convolution + downsampling (10m → 60-80m)
Radiometric	6-bit quantization, sqrt compression, saturation
Striping	6-detector gain/offset mismatch artifacts
Memory Effect	Bright target recovery trailing
Noise	Coherent (periodic) + random (gaussian/poisson)
Scan Artifacts	Line dropouts and transmission errors

API Reference

# Combined emulation (recommended)
mss, labels, sf = satharmony.emulate_s2_with_labels(image, labels, seed=42)

# Independent emulation
mss, sf = satharmony.emulate_s2(image, seed=42)
mss, sf = satharmony.emulate_s2(image, scale_factor=8)  # fixed scale
labels = satharmony.emulate_labels(labels, scale_factor=sf)

# Low-level access
from satharmony import MSSEmulator, PipelineConfig

config = PipelineConfig()
config.spatial.target_gsd.min = 60.0
config.spatial.target_gsd.max = 60.0  # fixed 60m

emulator = MSSEmulator(config)
mss = emulator(image)

Label Aggregation

CloudSEN12 labels (10m) are aggregated using conservative criteria from Roy et al.:

• Precedence: cloud > shadow > thin_cloud > clear
• Classes: 0=clear, 1=thin_cloud, 2=cloud, 3=shadow

Citation

@software{satharmony2025,
  author  = {Contreras, Julio},
  title   = {SatHarmony: Sensor Emulation for Earth Observation},
  year    = {2025},
  url     = {https://github.com/IPL-UV/satharmony}
}

License

MIT License - see LICENSE for details.