Nicola Falco

Nicola Falco

Research Topic: Analysis of hyperspectral remote sensing images for land cover classification. Feature selection/extraction techniques are exploited and developed in order to retrieve the most useful spectral-spatial information for the classification problem. My research activity includes also mathematical morphology, pattern recognition and change detection analysis in optical remote sensing data.

Parallel project: Change detection methods in optical remote sensing (funded by Rannis 2011). The aim of the project is to develop software for updating map based on change detection analysis.

Keywords: Hyperspectral/multispectral remote sensing images, land cover classification, feature selection/extraction, change detection.

Data sets: Hyperspectral images: AVIRIS, ROSIS, Hyperion, HYDICE.  Multispectral and Panchromatic images: Quickbird, IKONOS, SPOT5.