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Multispectral Algorithm Development
New multispectral sensors with higher spatial and spectral resolution require MDA image processing methods to fully utilize the extra information.
Even though multispectral data has been in use for decades and is the most widely used type of imagery to this day, new applications and algorithms are being developing continuously. Recently, some new multispectral sensors have radically changed in terms of spatial and spectral resolution. Higher spatial and/or spectral resolution requires new image processing methods to fully utilize the extra information. Every project has it's own unique set of challenges. By developing our own algorithms, we are free to move beyond what COTS packages are able to deliver. Even for many common image processing tasks included in COTS packages, MDA has proprietary algorithms to perform the task faster and better.
Data Fusion
Fusing high resolution panchromatic data with lower resolution multispectral data with COTS packages often leads to unrealistic colors, unsightly edges due to slight misregistrations, and is normally limited to three bands. MDA has developed a fusion algorithm that faithfully preserves colors, is forgiving of slight misregistrations, and operates on any number of bands simultaneously.
Vegitation Supression
For many earth scientists, vegetation is a major hindrance to the utilization of remotely sensed data because it masks the features they are trying to observe. MDA's vegetation suppression algorithm can "see through the trees," so to speak. By differentiating light reflected from vegetation and the ground underneath within a pixel, we are able to throw away the vegetation portion and show only the ground. Of course, this works best in open canopy type situations where a significant portion of the reflected light is coming from the ground.
Special Transforms
MDA has developed proprietary spectral transforms over the last several decades that far out perform those available to the community at large. Techniques such as EarthSat GeoVueTM and EarthSat MineralVueTM take all the available input spectral bands and produce a color image showing an incredible amount of spectral detail collected from each band. These methods differ from standard transforms, such as principal components, in that they are highly customizable and specific details (such as clouds, water, vegetation, etc.) may be either suppressed or enhanced depending on the goal of the user.
Minimum Noise Fraction (MNF)
The minimum noise fraction is a spectral transform that is a step beyond principal components. This algorithm is included with the ENVI COTS package. It separates noise from the signal and condenses much of the spectral information into a few bands that may then be used to create a color composite.
