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.
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