Satellite Imaging Technology (Remote Sensing) has led the way to the development of hyperspectral and multispectral sensors around the world, a tool that can be used to map specific materials by detecting specific chemical and material bonds from satellite and airborne sensors. Multispectral data acquired in space and by airborne sensors have been utilized extensively for the past many years in research projects dealing with such diverse problems as land cover and topographic mapping, physical and biological oceanography, and archaeology.

Research has expanded to include analysis of hyperspectral data acquired simultaneously in tens to hundreds of narrow channels. New algorithms have been developed both to exploit the spectral information of these sensors and to better deal with the computational demands of these enormous data sets. It is an excellent tool for environmental assessments, mineral mapping and land cover mapping, wildlife habitat monitoring and general land management studies.

Multispectral imaging often can include large data sets and require specialized processing methods. Hyperspectral data sets are generally composed of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm).

Actual detection of materials is dependent on the spectral coverage, spectral resolution, and signal-to-noise of the spectrometer, the abundance of the material and the strength of absorption features for that material in the wavelength region. In remote sensing situations, the surface materials mapped must be exposed in the optical surface and the diagnostic absorption features must be in regions of the spectrum that are reasonably transparent to the atmosphere.

Advanced image processing techniques from various satellite sensors such as color and panchromatic image data processing, orthorectification, pan sharpening with image data fusion, image enhancements, georeferencing, mosaicing, and color/grayscale balancing and is used in various applications.

Optional satellite imaging features may be incorporate with specialized processing procedures, which are used to analyze:

• Land cover classification and mapping
• LANDSAT 7 +ETM coastal seafloor mapping
• Extraction of culture data
• Coral reef detection and mapping
• Agriculture and Forestry production
• Normalized Difference Vegetation Index (NDVI) classification and mapping
• Lithological classification and mapping
• Change detection
• Environmental monitoring
• Urban development and monitoring
• Wildlife and Habitat Monitoring

Specialized imaging processing techniques are required to convert the apparent surface reflectance before analysis can take place. Atmospheric correction such as ATCOR (Atmospheric and Topographic Correction) techniques are used to retrieve physical parameters of the earth’s surface such as atmospheric conditions (emissivity, temperature), thermal and atmospheric radiance and transmittance functions to simulate the simplified properties of a 3D atmosphere.

Classification and feature extraction methods have been commonly used for many years for the mapping of minerals and vegetative cover of multispectral and hyperspectral data sets. Vector data structure is essential to most mapping, GIS (geographic information system), and CAD (computer aided design) software packages, which might export data to vector formats such as shape files, DXF, DWG, SVC, and ASV.

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