U N I V E R S I T À D E G L I S T U D I D I M I L A N O C17 SC for Environmental Applications and Remote Sensing I M S C I A Soft Computing for Environmental Applications and Remote Sensing Environmental applications Fabio Scotti - Manuel Roveri Università degli studi, Milano, Italy

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Introduction This lecture introduces examples of environmental applications of the Remote Sensing technologies In the first part of the lesson we proposes some examples of classical approaches In the second part of the lesson we describes some solutions solved using soft-computing techniques: –Oil Spill Detection; –Biomass measurement; –Satellite Cloud Classification.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Overview of Environmental applications –Agriculture ( Crop Type Mapping, Crop Monitoring) –Forestry ( Clear cut Mapping, Species identification, Burn Mapping) –Geology ( Structural Mapping, Geologic Units) –Hydrology ( Flood Delineation, Soil Moisture) –Sea Ice ( Type & concentration, Ice Motion) –Land Cover ( Rural/Urban change, Biomass Mapping) –Mapping ( Planimetry, DEMs, Topo Mapping) –Oceans & Coastal ( Ocean Features, Ocean Colour, Oil Spill Detection) Please read the tutorial L4_Enviro1.pdf (*) linked in the course page. (*) Canada Centre for Remote Sensing

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Neural Networks for Oil Spill Detection We mention here a neural network approach for semi-automatic detection of oil spills. The goal of the reading is to understand the application problem, the topology selection of the neural network, the creation of the datasets and the network testing phase. Please read carefully the paper L4_Enviro2.pdf (*) linked in the course page.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Neural Networks for biomass measurements In this application we have a neural network used to estimate the forest stand biomass. Interestingly the NNs has been considered in three situations: –trained on model data to invert model values; –trained on real data; –to invert actual measurements, and trained on simulated data to invert measured data. The goal of the reading is to understand the application problem, and the consider the adopted methodology to design the neural networks. Please read carefully the paper L4_Enviro3.pdf (*) linked in the course page.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Neural Networks for Satellite Cloud Classification In this application we have a neural network used to classify clouds from satellite images. A temporal updating approach for probabilistic neural network (PNN) classifiers was developed to account for temporal changes of spectral and temperature features of clouds in the visible and infrared. The goal of the reading is to understand the application problem, and the consider the adopted methodology to design the neural networks. Please read carefully the paper L4_Enviro4.pdf (*) linked in the course page.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri End of the lecture