Radiometric Correction and Image Enhancement

1. Radiometric Correction and Image Enhancement Modifying digital numbers

2. Why modify digital numbers (DNs)? • Improve the way an image looks • Reduce “noise” in an image • Fill in data gaps • Normalize digital numbers across a scene (remove effects due to terrain, atmosphere) • Normalize two or more images for multi-temporal comparison • Calculate biophysical values for each pixel (radiance, reflectance, net primary productivity)

3. What is radiometric correction? • Radiometric correction attempts to eliminate the effects on a pixel’s value by elements that are not part of the target • These effects are commonly grouped as: • Sensor influences • Terrain effects • Atmospheric effects

4. Some considerations? • Often a trade-off between benefit derived from the correction and resources (time and money) required to accurately implement the algorithm • Applying a radiometric correction does not necessarily produce accurate results • Good idea to calculate top of the atmosphere reflectance since it is easy and standardizes pixel values to a physical measurement • Using simple correction methods may be sufficient for the task at hand • Always analyze the output from an atmospheric correction application to see if the results make sense

5. Ideal (laboratory) environment • Clean filtered air • Precise control over detector and illumination source position • Distance light travels from source to detector is short • Precise control over illumination intensity • Sample carefully prepared

6. Actual environment • Particulates in the atmosphere attenuate the signal (light from the sun) • Terrain and feature structure very complex making it difficult to precisely model the path of light

8. Different types of radiometric adjustments • Image enhancement • Remove sensor anomalies • Remove illumination/target/detector configuration effects (topographic effects) • Remove atmospheric effects • Remove topographic effects • Calibration - convert digital counts to bio-physical parameters • Normalization – Equalize digital numbers for two or more images

9. Image enhancement • Goal is to either make the image look nice or to improve interpretability • Uses image scaling, filters, and spectral techniques • Image scaling aims to distribute pixel values across the full range of possible values • Filters are used to increase contrast between different feature or to reduce noise in an image • Spectral transformations synthesize multiple bands into a new band or set of bands • Most image processing software programs have simple tools to apply these enhancements

10. Sensor anomalies • Line dropout is an occasional problem and can be “fixed” by averaging using data from the line above and below the “bad” line • Sensors are calibrated pre and post-launch • Striping caused by a variable response for different sensors and is less of an issue for modern imagery since it is compensated for on-board the satellite or during initial processing

11. Illumination/target/detector configuration effects • Goal is to simulate flat terrain so brightly illuminated shadowed areas look similar • An image in rough terrain will look much flatter as shadow effects are reduced • Most approaches use a digital elevation model (DEM) to model the terrain and cosine corrections to correct illumination • Important for the DEM to have similar or finer resolution than the satellite image

12. Uncorrected Corrected Images from Introductory digital image processing: A remote sensing perspective, John R. Jensen

13. Atmospheric effects • Atmospheric effects can range from slight to complete obstruction of the intended target (clouds) • Atmospheric effects are often variable across an image • Some approaches (radiative transfer modeling) require atmospheric measurements acquired at the same time as image acquisition (i.e., ATCOR) • Other approaches use only image processing methods and do not require in-situ measurements (i.e., COST)

14. Calibration • Goal is to convert DN to a biophysical values such as radiance, surface reflectance, or net primary productivity • Calculating at-sensor radiance is often done by the image provider although the calibration coefficients (gain and offset) must be applied by the image user • At-sensor reflectance calculations standardizes illumination geometry and requires that you know the date and time of acquisition (to calculate solar zenith angle), Earth-sun distance and sensor specific data such as solar spectral irradiances • Determining surface reflectance is often very difficult

15. Normalization • Helpful when comparing imagery from different dates • Matching pseudoinvariant ground targets using regression is a common method • Does not typically compensate for differences between seasons (i.e., wet and dry)

16. COST algorithm background • An improvement over the dark object subtraction (DOS) method • Incorporates a cosine correction for solar zenith angle • Developed by Pat S. Chavez – USGS • Citation: Chavez, P.S. Jr., 1996, Image-based atmospheric corrections—revisited and revised. Photogrammetric Engineering and Remote Sensing 62(9): 1025-1036.

17. COST algorithm Where, BandN = Reflectance for Band N GainBandN = Gain for Band N (from Landsat guide) BiasBandN = Bias for Band N (from Landsat guide) LbandN = Digital Number for Band N HbandN = Digital Number representing Dark Object for Band N D = Normalized Earth-Sun Distance EbandN = Solar Irradiance for Band N  = Atmospheric Transmittance expressed as

18. ERDAS implementation of COST • Web site to create ERDAS model (.gmd file) for Landsat TM images: http://www.gis.usu.edu/docs/projects/swgap/ImageStandardization.htm • The model created from the web site above can be opened in the ERDAS “Model Maker” • The Landsat image file must be in NLAPS format with the header (.h1) file • If the image format is not in the NLAPS format an existing model can be manually edited