Panchromatic Sharpening

Panchromatic sharpening is simple in eemont!

Before anything, let’s import our modules and authenticate in Google Earth Engine:

import ee, eemont

ee.Authenticate()
ee.Initialize()

Now, we are ready to go!

Overview

The eemont package extends the ee.Image and ee.ImageCollection classes with the method panSharpen():

ee.Image

panSharpen(self[, method, qa])

Apply panchromatic sharpening to the Image.

ee.ImageCollection

panSharpen(self[, method, qa])

Apply panchromatic sharpening to each Image in the Image Collection.

Supported Platforms

Pansharpening is supported for the following platforms:

Landsat Missions

Warning

Surface Reflectance products do not contain panchromatic bands and do not support pan-sharpening.

Algorithms

The panSharpen method can be run using a variety of different algorithms by setting the method argument. The following sharpening algorithms are supported by eemont:

Available algorithm methods.
Method	Name	kwargs
`SFIM` (default)	Smoothing-Filter Based Intensity Modulation	No
`HPFA`	High-Pass Filter Addition	No
`PCS`	Principal Component Substitution	Yes
`SM`	Simple Mean	No

Usage

Let’s load a supported image from Landsat 8:

img = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819")

And sharpen it using the panSharpen method with the default SFIM algorithm.

sharp = img.panSharpen()

Easy as that!

We can also try sharpening with a different algorithm. Remember that some algorirthms take additional keyword arguments. Here, we’ll specify maxPixels to avoid pixel limitations with the PCS algorithm.

sharp = img.panSharpen(method="PCS", maxPixels=1e13)

Pan-sharpening image collections is identical to sharpening images:

imgCollection = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA")
sharpCollection = imgCollection.panSharpen()

Quality Assessment

Quality assessment (QA) metrics can be calculated by the panSharpen method to measure how much spectral distortion was introduced by sharpening and to compare different sharpening algorithms. eemont supports the following QA metrics:

Available QA metrics.
QA	Name	Ideal Value	Mode
`MSE`	Mean Squared Error	0	Band
`RMSE`	Root-Mean Squared Error	0	Band
`RASE`	Relative Average Spectral Error	0	Image
`ERGAS`	Dimensionless Global Relative Error of Synthesis	0	Image
`DIV`	Difference in Variance	0	Band
`bias`	Bias	0	Band
`CC`	Correlation Coefficient	1	Band
`CML`	Change in mean luminance	1	Band
`CMC`	Change in mean contrast	1	Band
`UIQI`	Universal Image Quality Index	1	Band

Note

Some metrics are calculated image-wise and others are calculated band-wise. Image-wise metrics return one value for the entire image while band-wise metrics return one value for each band.

QA metrics are calculated by passing a list of one or more metrics to the qa argument of the panSharpen method. Below, we’ll calculate RASE and UIQI while sharpening an image.

metrics = ["RASE", "UIQI"]
img = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819")
sharp = img.panSharpen(qa=metrics, maxPixels=1e13)