Brovey Pansharpening

Overview

Satellite sensors capture high-resolution panchromatic (single-band) and lower-resolution multispectral images. Pansharpening fuses both to get high-resolution color imagery. The Brovey transform normalizes each band by a weighted sum, then multiplies by the panchromatic intensity. This preserves spectral ratios while injecting spatial detail.

Quick Start

import numpy as np
from skimage.transform import rescale

def brovey_pansharpen(ms_bands, pan, nir_weight=0.1):
    R, G, B, NIR = ms_bands
    dnf = (pan - nir_weight * NIR) / (
        nir_weight * R + nir_weight * G + nir_weight * B + 1e-10)
    sharp = np.stack([R * dnf, G * dnf, B * dnf, NIR * dnf], axis=-1)
    return np.clip(sharp, 0, None)

ms_upscaled = np.stack([
    rescale(ms[i], 4, order=3) for i in range(4)], axis=-1)
ms_upscaled = ms_upscaled[:pan.shape[0], :pan.shape[1]]
result = brovey_pansharpen(
    [ms_upscaled[..., i] for i in range(4)], pan)

Workflow

1. Upscale multispectral bands to match panchromatic resolution (typically 4x bicubic)
2. Crop to align dimensions if sizes differ by a pixel
3. Compute per-pixel normalization factor from pan and weighted band sum
4. Multiply each band by the normalization factor
5. Clip negatives to zero

Key Decisions

• NIR weight (W): 0.1 is typical; higher values reduce NIR influence on the ratio
• vs HSV pansharpening: Brovey preserves spectral ratios better; HSV preserves hue but can shift brightness
• Upscale method: bicubic (order=3) balances sharpness and artifacts
• Band alignment: always crop after rescale — off-by-one pixels cause misregistration

References

• Panchromatic sharpening