Kun-Shan Chen National Central University Center for Space and Remote Sensing Research Chung-Li, Taiwan 32054 E-mail: [email protected] Abstract. The multiple-classifiers approach is utilized to fully take into account the complementary and supplementary information from different data sources for terrain cover classification. To combine the outputs of classifiers that may be conditional...

Sustainability of the global environment is dependent on the accurate land cover information over large areas. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets were derived from a pixel-based singledate multi-spectral rem...

We propose a supervised nonparametric technique, based on the “compound classification rule” for minimum error, to detect land-cover transitions between two remote-sensing images acquired at different times. Thanks to a simplifying hypothesis, the compound classification rule is transformed into a form easier to compute. In the obtained rule, an important role is played by the probabilities of ...

Nowadays, support vector machines (SVM) are receiving increasing attention in land cover/use classification although one of the major drawbacks of the technique is the kernel function selection and its parameters setting. In this paper, a novel SVM parameters optimization method based on selfadaptive mutation particle swarm optimizer (SAMPSO-SVM) is proposed to improve the generalization perfor...