Remote sensing acquires and interprets small or large-scale data about the Earth from a distance. Using a wide range of spatial, spectral, temporal, and radiometric scales remote sensing is a large and diverse field for which this Handbook will be the key research reference. Illustrated throughout, an essential resource for the analysis of remotely sensed data, The SAGE Handbook of Remote Sensing provides researchers with a definitive statement of the core concepts and methodologies in the discipline.
Chapter 19: Image Classification
artificial neural networks, decision trees, machine learning, maximum likelihood, object oriented classification, support vector machines, spectral mixture analysis.
Humans have been able to visually identify different types of land-use and land-cover in aerial photography ever since they were first acquired in the mid-1800s. Human visual image classification is based on the use of the fundamental elements of image interpretation such as size, shape, shadow, tone, color, texture, pattern, site, association, etc. Visual image classification is still very important and is performed by millions of people every day as they browse remote sensing images on the internet (e.g., using Google Earth or Virtual Earth) to identify features of interest.
Since the mid-1960s, humans have been able to extract land-use/land-cover and biophysical information directly from remote ...