Remote Sensing image analysis is mostly done using only spectral
information on a pixel by pixel basis. Information captured in
neighbouring cells, or information about patterns surrounding the pixel
of interest often provides useful supplementary information. This book
presents a wide range of innovative and advanced image processing
methods for including spatial information, captured by neighbouring
pixels in remotely sensed images, to improve image interpretation or
image classification. Presented methods include different types of
variogram analysis, various methods for texture quantification, smart
kernel operators, pattern recognition techniques, image segmentation
methods, sub-pixel methods, wavelets and advanced spectral mixture
analysis techniques. Apart from explaining the working methods in detail
a wide range of applications is presented covering land cover and land
use mapping, environmental applications such as heavy metal pollution,
urban mapping and geological applications to detect hydrocarbon seeps.
The book is meant for professionals, PhD students and graduates who use
remote sensing image analysis, image interpretation and image
classification in their work related to disciplines such as geography,
geology, botany, ecology, forestry, cartography, soil science,
engineering and urban and regional planning.