9 docs tagged with "General Info"

AVIRIS / AVIRIS-NG

In this section, we will talk about two different processing levels, L1B Top of Atmosphere Radiance, and L2A Bottom

Before we dive head first into hyperspectral imagery, it's best to understand some of the fundamentals of geospatial processing. Wyvern's satellites produce raster imagery, which can then be used by geospatial analysts & tools like QGIS and Python to extract value from the data.

Hyperspectral imaging is a relatively new technology to be commercialized. It is a type of imaging that has

Hyperspectral imagery is similar to most other forms of geospatial imagery, commonly called rasters. The main difference between images generated by satellites like Landsat & Sentinel, and hyperspectral imagery is the number of bands within an image. The below table shows common sources of imagery and their bands. You can see our satellites will have a ton more bands than common multi-spectral satellites. Take note, this also means larger file sizes!

Hyperspectral imaging uses a narrow range of wavelengths across the electromagnetic spectrum, to produce images of the Earth's surface. These images contain information about the reflectance of each pixel across the electromagnetic spectrum. How each pixel reflects light can tell us a lot about the materials that are present within a pixel.

Every material and compound reflects light differently based on it's chemical composition. Many organizations have collected, cataloged, and published these reflectance spectra for use in analysis and detection.

An Earth observation sensor reads the intensity of the electromagnetic spectrum collected within each pixel as a digital number (DN). These DNs represent the surface reflectance from the Earth plus contributions from atmospheric gas absorption, atmospheric scattering, variations in illumination from topographical features, instrument response curves and other artifacts. This is to say, DNs are not surface reflectance! DNs are data that require radiometric processing in order to obtain images with physically meaningful quantities like radiance or reflectance.

We leverage the SpatioTemporal Asset Catalog (STAC) format to make our geospatial data more accessible, interoperable, and easier to discover.