These white papers offer new perspectives –
The CMAC algorithm built into the RESOLV SaaS exposes a different view for atmospheric correction. This series of white papers provides documentation and a number of unique insights gained through the CMAC’s different approach.
WHITE PAPER 1
Why Correct VNIR Satellite Imagery to Surface Reflectance?
Atmospheric correction is a final image processing step converting top-of-atmosphere (TOAR) satellite images to surface reflectance. This conversion supports clear viewing, machine analysis and AI. Surface reflectance clears haze, retrieves the true digital signal, and is a necessary step for automated change detection crucial for ISR analyses. Surface reflectance conversion standardizes satellite imagery for comparison among dates and sensor platforms.
VNIR satellite images for ISR are often applied as top-of-atmosphere reflectance (TOAR), i.e., without correction to surface reflectance. This white paper demonstrates that surface reflectance significantly enhances the dynamic range of reflectance. In comparison, TOAR can vary widely depending upon the atmospheric aerosol burden, so it is unsuitable for accurate ISR application.
WHITE PAPER 2
Atmospheric Correction for Ocean Imagery
Correction of satellite imagery over the ocean is an important step that renders images clear for visual interpretation and with the digital signal restored to support machine analyses, including AI. Satellite imagery over the ocean is often degraded by haze due to water droplets released through wind and wave interaction, salt grains evaporated from them, and dust and smoke advected from land.
RESOLV-w is an adaptation of the RESOLV terrestrial version that must first be calibrated to provide standardized, automated output of surface reflectance from top-of-atmosphere ocean images. The completely automated RESOLV-w workflow preserves ship color and features to the limitations of image resolution. Corrected images are output with bordering that provides automated adaptive GIS display for rapid image inspection and visual confirmation. This added utility does not alter the digital data.
WHITE PAPER 3
Mapping Atmospheric Effect Grayscales with Scene Statistics
“Atmospheric effect” is a general term we use to characterize atmospheric clarity as a lump sum to enable reversing the scatter and absorption caused by light transmission through Earth’s atmosphere. Existing atmospheric correction software, for example LaSRC for Landsat, apply ancillary data generated by another satellite, such as MODIS (Moderate Resolution Imaging Spectroradiometer). An ancillary data requirement to represent atmospheric effect is a significant impediment for smallsat and higher resolution applications because MODIS data have coarse spatial resolution and delay data processing. A more serious problem arises through mismatched overpass times during which conditions can change. This mismatch, potentially by hours, greatly increases atmospheric correction uncertainty and directly affects reliability. CMAC avoids these problems by assessing spatially discrete atmospheric effect using image band statistics.
CMAC’s atmospheric effect is modeled as an index that represents the increase of low blue reflectance of alfalfa caused by aerosol backscatter. When displayed as a raster, Atm-I values portray a grayscale map whose brightness indicates the degree of atmospheric effect upon reflectance that serves as a scalar for reversing the atmospheric effect.
WHITE PAPER 4
Reversing Atmospheric Effect to Surface Reflectance
CMAC development began with a unique discovery: cumulative distribution functions (CDFs) for TOAR change in a structured pattern due to aerosol loading that we initially judged by the degree of apparent haze. This gave a clue for returning TOAR to surface reflectance by reversing the atmospheric effect that created the changes incorporated in the TOAR display. Comparing clear and hazy images for the same AOI across short time spans showed that increasing haze causes reflectance CDFs to rotate counterclockwise, and for decreasing haze, to rotate clockwise. Under increasing haze, dark reflectance became brighter from backscattered light off aerosol particles, while bright reflectance became darker due absorbance. Somewhere between dark and bright endmembers, backscatter and absorbance cancel and surface reflectance and TOAR remain equivalent. We translated this effect into a conceptual model by inverting and adjusting the axes of the empirical line method, a well-known EO image calibration procedure.
The CMAC conceptual model summarizes atmospheric effect as a linear relationship whose slope and offset are the two parameters used to reverse the atmospheric effect grayscales to deliver surface reflectance from TOAR.
Join us as we explore atmospheric correction of satellite images and how this unlocks the data for precision applications.
In our commitment to excellence, RESOLV has undergone rigorous comparative analysis against established atmospheric correction software, benchmarked against the widely accepted packages LaSRC and Sen2Cor. RESOLV has also been successfully applied to high-resolution smallsat data.
RESOLV represents a completely new approach. It begins by mapping the atmospheric effect across each image as a grayscale input, which is then processed through an algorithm based on observations of light behavior after it passes through Earth’s atmosphere. This algorithm modifies and inverts the traditional empirical line method, resulting in a closed-form equation that enables lightning-fast calculations. With this structure, RESOLV delivers the most accurate estimates of surface reflectance for any image, anywhere.
Key findings from the comparison with competing methods include:
- Proven Accuracy and Reproducibility: RESOLV consistently delivers highly accurate surface reflectance retrievals, even under challenging atmospheric conditions, outperforming traditional methods.
- Designed Efficiency: RESOLV’s architecture ensures rapid processing, providing near real-time surface reflectance data with exceptional precision.
- Future-Ready Versatility: RESOLV goes beyond proof-of-concept and has demonstrated adaptability, seamlessly integrating with a wide variety of Earth observation sensors and platforms.
Join Us on This Journey
RESOLV is more than just a service—it’s a movement
towards redefining the standards of smallsat imagery analysis.
We invite you to be a part of this journey.
Together, let’s unlock the full potential of satellite imagery.