Correct even the most challenging atmospheric conditions.

Today, remotely sensed imagery is used in applications from precision agricultural to reconnaissance and military surveillance. As an image scientist, you need an accurate representation of a land area, whether you're analyzing vegetation, locating an object, or studying change in a scene over time. Accurate results can be a challenge, since most remotely sensed imagery contains "noise," caused by atmospheric particles that act as contaminants, obscuring the image and making analyses unreliable.

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Now, the ENVI FLAASH Module allows you to remove the most challenging atmospheric conditions from your imagery, so you can be confident you're using the most accurate data available. The module is based on proven MODTRAN code and algorithms, producing accurate results based on every image's unique parameters.

Compared with other tools, which offer users pre-calculated models based on generic atmospheric conditions, FLAASH treats each image and its atmospheric imprint individually, creating a unique model every time. That extra accuracy ensures that the resulting data are the most advanced, reliable representation of an image scene.

The most accurate representation of your data

• A distinctive data polishing technique for realistic representations
• An adjacency correction method to fix images with high contrast areas, that produce “mixed” signatures, or artifacts

• A wavelength recalibration process corrects misplaced wavelengths
• A method that treats each pixel individually