
Pattern Recognition Methods for Crop Classification from Hyperspectral Remote Sensing Images
Autor: Luis Gomez Chova
Número de Páginas: 186(Complete work in Spanish) Remote sensing aerial spectral imaging was one of the first application areas where spectral imaging was used in order to identify and monitor the natural resources and covers on earth surface. Aerial spectral imaging is being developed with the aim of monitoring natural resources like coastal areas, forestry and extensive crops. The information contained in hyperspectral images allows the reconstruction of the energy curve radiated by the terrestrial surface throughout the electromagnetic spectrum. Hence, the characterization, identification and classification of the observed material from their spectral curve is an interesting possibility. Pattern recognition methods have proven to be effective techniques in this kind of applications. In fact, classification of surface features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Nevertheless, the main problem with supervised methods is that the learning process...