Development of a methodology for spatial composite indicators: a case study on landscape.

This thesis proposes a methodology for the construction of spatial composite indicators (SCI). The study starts from the premise that Composite Indicators (CIs) are regarded as very reliable tools to support decision processes. They are usually developed to describe complex phenomena of the reality in various domains, and more specifically, to rank spatial units (usually countries) in which a given indicator is calculated. Despite their wide use and their development, no attention has generally been paid to the spatial dimension of their input data and of their final score. Data are treated as normal statistical sampling, therefore their spatial structure and their spatial importance are considered to be equal across the spatial domain, without considerations about possible spatial variations. Nowadays, this appears to be a serious limit, considering the development of Spatial Data Infrastructures (SDI), which makes a large amount of spatial data available, and the development of spatial statistical techniques implemented in GIS, with combined together offer unprecedented opportunity for the spatialization of CIs

Feature evolution for classification of remotely sensed data

In a number of remote-sensing applications,. it is critical to decrease the dimensionality of the input in order to reduce the complexity and, hence, the processing time and possibly improve classification accuracy. In this letter, the application of genetic algorithms as a means of feature selection is explored. A genetic algorithm is used to select a near-optimal subset of input dimensions using a feed-forward multilayer perceptron trained by backpropagation as the classifier. Feature and topology evolution are performed simultaneously based on actual classification results (wrapper approach)

Feature Evolution for Classification of Remotely Sensed Data

In a number of remote sensing applications it is critical to decrease the dimensionality of the input in order to reduce the complexity and hence the processing time and possibly improve classification accuracy. In this paper the application of genetic algorithms as a means of feature selection is explored. A genetic algorithm is used to select a near-optimal subset of input dimensions using a feed forward multilayer perceptron trained by backpropagation as the classifier. Feature and topology evolution are performed simultaneously based on actual classification results (wrapper approach).JRC.G.3-Agricultur