Advanced Image Processing Techniques for Remotely Sensed by Professor Dr. Pramod K. Varshney, Dr. Manoj K. Arora (auth.)

By Professor Dr. Pramod K. Varshney, Dr. Manoj K. Arora (auth.)

Over the final fifty years, a number of spaceborne and airborne sensors were hired to collect information about the earth's floor and atmosphere. As sensor know-how keeps to strengthen, distant sensing information with superior temporal, spectral, and spatial solution is changing into extra on hand. This frequent availability of huge quantities of information has necessitated the advance of effective info processing ideas for a wide selection of purposes. particularly, nice strides were made within the improvement of electronic photo processing concepts for distant sensing information. The target has been effective dealing with of great quantities of information, fusion of knowledge from various sensors, class for photograph interpretation, and improvement of easy items that permit wealthy visualization. This booklet provides a few new algorithms which were constructed for prime­ dimensional datasets, equivalent to multispectral and hyperspectral imagery. The contents of the booklet are dependent totally on examine performed by way of a few individuals and alumni of the Sensor Fusion Laboratory at Syracuse University.

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The AVIRIS was modified subsequently and flown throughout North America and Eurasia in the following 4 years and, since this period, this sensor has continued to be improved and is still providing some of the highest quality data to the scientific community today. Parallel to NASA's development of the AVIRIS sensor have been a number of commercial ventures where hyperspectral sensors have been designed and deployed. Since 1978, the Canadian company ITRES has paved the way for airborne hyperspectral remote sensing through the development of the Compact Airborne Spectrographic Imager (CASI), which was based on the earlier FLI (Hollinger et al.

Sensors operating across the full spectral range include the AVIRIS (224 wavebands) and HyMap (l26 wavebands). Each of these sensors provides contiguous spectral coverage, including those regions where absorption by atmospheric water occurs, facilitating atmospheric absorption effects to be calculated. More recently, sensors designed to observe across the full reflected spectral range plus the TIR wavelength range have become operational, including the DAIS-7915 (Ben-Dor et al. 2002). The spectral resolution and sampling interval are critical in determining the accuracy to which features in the radiation spectra can be measured.

Modified partial least squares (MPLS), neural network, statistical methods (Niemann and Goodenough 2003) and model inversion techniques (Demarez and Gastellu-Etchegorry 2000; Jacquemoud et al. 2000) have also been used to assist retrieval. MPLS has proved particularly useful as the information content of hundreds of bands can be concentrated within a few variables, although the optimal predictive bands still need to be identified using correlograms of spectra and biochemical concentration or regression equations from the MPLS analysis.

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