Radiometric and Atmospheric Correction

Introduction:

Before analysis can be performed on satellite imagery, it needs to be atmospherically corrected. In this lab we will be using three different methods: Empirical Line Calibration, Dark Object Subtraction, and Multidate Image Normalization.

Methods:

Empirical Line Calibration (ELC) was performed by developing regression equations between in situ reflectance measurements and those recorded by the sensor for the same target. In order to perform ELC, I selected 5 areas within the image, then found in situ reflectance information from spectral libraries within Erdas Imagine.



The next method used was Dark Object Subtraction (DOS). The satellite image is converted to at-satellite spectral radiance, before it is converted to true surface reflectance. The radiance conversion is performed by analyzing the image metadata in order to determine the original and re-scaled pixel values. The conversion to reflectance is done by calibrating the radiance image based on: the distance between the earth and the sun, atmospheric transmittance between the ground and sensor, the sun zenith angle, atmospheric transmittance from the sun to ground, and the mean atmospheric spectral irradiance.

Image 1: Comparing the ground control points.

The final correction method used was Multidate Image Normalization (MIN). The first step was to  collect radiometric ground control points from the base image and subsequent image (Image 1). The points were used to build regression equations in Excel. The sample points were collected from static, non-vegetated areas throughout the image scene.

Results:

ELC didn’t really seem to change any pixel values by major amounts (Image 2).

Image 2: The original image (left), and ELC corrected image (right).

DOS increased the visible contrast between features, and eliminated atmospheric haze (Image 3).

Image 3: The original image (left) and DOS corrected image (right) viewed using a 7,5,3 band combination.

The MIN method greatly reduced the visible haze on the imagery, and produced more vivid values overall.

Image 4: The Chicago 2000 (left) and Chicago 2009 MIN (right) viewed using a 7,5,3 band combination.

In the future, I would be more likely use Dark Object Subtraction than the other methods, as it produced a more accurate result. However, Multidate Image Normalization may also be used if I am performing analysis over multiple time periods.