By Professor Dr. Pramod K. Varshney, Dr. Manoj K. Arora (auth.)
Over the final fifty years, a great number of spaceborne and airborne sensors were hired to assemble information about the earth's floor and atmosphere. As sensor know-how keeps to increase, distant sensing info with greater temporal, spectral, and spatial answer is turning into extra on hand. This frequent availability of huge quantities of information has necessitated the advance of effective info processing concepts for a large choice of purposes. specifically, nice strides were made within the improvement of electronic photo processing recommendations for distant sensing information. The aim has been effective dealing with of mammoth quantities of information, fusion of knowledge from varied sensors, class for snapshot interpretation, and improvement of common items that let wealthy visualization. This ebook provides a few new algorithms which have been built for prime dimensional datasets, akin to multispectral and hyperspectral imagery. The contents of the ebook are dependent totally on study performed by way of a few contributors and alumni of the Sensor Fusion Laboratory at Syracuse University.
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Extra resources for Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data
E. 400-2500 nm), many sensors operate only within the VNIR regions. As examples, the ASAS and CAS I record reflectance in the 20 1: Richard lucas, Aled Rowlands, Olaf Niemann, Ray Merton 404-1020 nm and 400-1050 nm regions respectively. The SASI operates only in the SWIR (from 820-2500 nm). 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.
Today, hyperspectral data are increasingly used for applications ranging from atmospheric characterisation and climate research, snow and ice hydrology, monitoring of coastal environments, understanding the structure and functioning of ecosystems, mineral exploration and land use, land cover and vegetation mapping. The following provides a brief overview of these applications. 1 Atmosphere and Hydrosphere Within the atmospheric sciences, hyperspectral remote sensing has been used primarily to investigate the retrieval of atmospheric properties, thereby allowing the development and implementation of correction techniques for airborne and spaceborne remote sensing data (Curran 1994; Green et al.
2001) or the use of empirical relationships and vegetation indices (Treitz and Howarth 1999). Other specific techniques include derivative spectra, continuum removal, hierarchical foreground/background analysis and SMA (Gamon and Qiu 1999), with most associated with either physically-based canopy radiation models (Treitz and Howarth 1999) or empirical spectral relationships (indices). 3 Vegetation Mapping Vegetation discrimination has been highlighted as one of the benefits of using hyperspectral data and the establishment of spectral libraries for different species has been suggested (Roberts et al.
Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data by Professor Dr. Pramod K. Varshney, Dr. Manoj K. Arora (auth.)