Evidential reasoning approach to multisource-data classification in remote sensing

In the evidential reasoning approach to the classification of remotely sensed multisource data, each data source is considered as providing a body of evidence with a certain degree of belief. The degrees of belief are represented by “interval-valued probabilities” rather than by conventional point-valued probabilities so that uncertainty can be embedded in the measures. The proposed method is applied to the ground-cover classification of simulated 201-band high resolution imaging spectrometer (HIRIS) data, from which a set of multiple sources is obtained by dividing the dimensionally huge data into smaller pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than conventional maximum likelihood methods