Pattern recognition is concerned with a range of information processing issues associated with the description or classification of measurements. It is based on a broad and often loosely related body of literature and techniques (Schalkoff 1992). Although statistical and structural (syntactic) approaches have dominated the subject there has been a growing interest in the use of neural networks for pattern recognition applications (Schalkoff 1992; Bishop 1995). This is particularly evident in relation to pattern recognition applications in remote sensing. Remote sensing is often used to derive information on the environment (Campbell 1996; Lillesand and Kiefer 2000). For example, satellite remote sensors are commonly used to provide images of the Earth’s surface that may be analysed to yield information on a diverse range of issues of ecological significance. This includes information on a variety of important environmental phenomena including land cover and its dynamics (e.g. deforestation), vegetation productivity and yield, soil water content, water quality and variation in surface temperature. Remote sensing has, therefore, the potential to provide information to support ecological research, particularly that addressing macro or coarse spatial scale issues (Roughgarden et al. 1991; Kasischke et al. 1997; Lucas and Curran 1999; Hall 2000). The value of remote sensing as a source of information on the environment is, however, sometimes limited by the image analysis techniques used. Traditionally, statistical techniques have been used widely in the analysis of remotely sensed data. However, in common with other fields of study, including ecology, the last 10–15 years has witnessed a rapid growth in the use of neural networks (Atkinson and Tatnall 1997; Lek et al. 2000). This chapter aims to report on some of the main application of neural networks in remote sensing and indicate topics where further development may be expected to occur.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
- Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks
G. M. Foody
- Springer Berlin Heidelberg
- Chapter 20