A. Burini

TerraSAR-X/SPOT-5 Fused Images for Supervised Land Cover Classification

This paper reports the study of supervised neural network algorithm for classification purposes. SPOT 5 and TerraSAR-X dataset are analyzed. Classification results are critically discussed and compared to ground truth map and unsupervised neural classification of the same area. The aim is to demonstrate the capability of neural networks in managing heterogeneous dataset and the accuracy improvement obtained by the use of the textural object based layers fused with the optical and radar data.

Sensitivity of multi-temporal high resolution polarimetric C and L-band SAR to grapes in vineyards

In the follow-up of the BACCHUS project, aimed at establishing a reference high quality geographic information system for vineyards, an airborne SAR survey has been carried out in fall 2005 in the Frascati area, near Rome (Italy) to investigate on the potential of radar remote sensing in vineyard monitoring. This contribution reports on the polarimetric very- high resolution C and L-band SAR data acquisition campaign supported by ESA and carried out by the DLR E-SAR on two dates in October 2005. The possible relation between the observed variations of backscattering at different polarizations and the harvest of the grapes is discussed.

Multi-temporal High-resolution Polarimetric L-band SAR Observation of a Wine-producing Landscape

In continuation of the BACCHUS project, aimed at establishing a reference high quality geographic information system for vineyards, an airborne SAR survey has been carried out in fall 2005 in the Frascati area, near Rome (Italy) to demonstrate the potential of airborne radar remote sensing in vineyard characterization. This contribution reports on the polarimetric L-band and dual polarization C-band SAR data acquisition campaign supported by ESA and carried out on two dates in October 2005 (the first during the grape harvest and the other after the vintage completion).

Dedicated neural networks algorithms for direct estimation of tropospheric ozone from satellite measurements

In this paper we report on the design of a Neural Networks algorithm to retrieve tropospheric ozone information from satellite data. Following a combined radiative transfer model-extended pruning sensitivity analysis for input wavelengths selection, we first made an inversion exercise based on a synthetically produced radiance-tropospheric ozone concentrations database. Starting from the encouraging obtained results, we tested the Net on ESA-ENVISAT SCIAMACHY Level lb data. A time series of Tropospheric Ozone Columns on some midlatitude sites has been retrieved from the satellite measurements and then compared with collocated and simultaneous ozonesondes reference columns. The inversion results are presented and critically discussed.

Fusion of High Resolution Polarimetric SAR and Multi-Spectral Optical Data for Precision Viticulure

In the follow-up of the BACCHUS project, aimed at establishing a reference high quality geographic information system for vineyards, an airborne SAR survey has been carried out in fall 2005 in the Frascati area, near Rome (Italy) to investigate on the potential of radar remote sensing in vineyard monitoring. This contribution reports the joint use of high resolution polarimetric SAR data and QuickBird optical data in order to evaluate the potential of remote sensing in vineyard detection and bio-physical parameters retrieval.

A neural approach to unsupervised classification of very-high resolution polarimetric SAR data

Analysis of L-band polarimetric SAR data has not been extensively carried out for undulating, heterogeneous and fragmented landscapes, where classification can become quite challenging. This paper reports results of a study on the pixel-by- pixel unsupervised classification of very-high resolution polarimetric images by self-organizing neural networks.

Urban land cover classification: potential of high and very-high resolution SAR imagery

A comparative study on the complexity of the urban environments in SAR imagery at different spatial resolutions is presented. Two datasets have been considered, including the city of Rome, Italy imaged in decametric resolution and the Frascati area (Rome, Italy) acquired in very high spatial resolution (~2 m) at L-band in a fully polarimetric mode. The different characteristics of the radar sensors require careful managing of the corresponding product capabilities to maximize the various pieces of information contained in the variety of scattering mechanisms.

SAR in agriculture: sensitivity of backscattering to grapes

The sensitivity of backscattering to grape development has been tested within a multi-temporal SAR power image approach to vineyard phenology monitoring. A simple model relating the backscattering coefficient of vineyards to the grapes biomass has been worked out and used to single out the effect of fruits. To this end, the information content of time series of the differences the σ 0 of the vineyards and those of reference agjacent uncultivated pixels has been exployted. The interpretation of the results benefits from the availability both of the G.I.S. database compiled within the ESA Bacchus projects and of ground truth, including information on soil roughness and plant status. Index Terms - SAR, vineyards, biomass.

TerraSAR-X Imaging for Unsepervised Land Cover Classification and Fire Mapping

Since a few months TerraSAR-X has been acquiring X-band SAR images of the earth surface from space. This contribution reports on a study carried out to understand the main textural features of the X-band radar return from various kinds of surfaces and in particular to assess the potential of images acquired by X-band space borne radars in mapping fire scars and in classifying suburban/agricultural land cover. To this end, a novel unsupervised neural network algorithm, the Textural Self-Organizing Map (TexSOM), based on the textural features of the radar image, has been worked out and tested on areas in Greece and Italy.

Solar Radiance Estimation by Means of MeteoSat 2nd Generation and Neural Processing: A Vineyard Precision Farming Case

We propose the use of MSG images for local ground radiance estimation. To this end, first the presence of clouds must be detected, then the satellite measurements must be correlated with measurements at ground. A Cloud Detection Net (CDN) algorithm has been implemented for the first task, while time series of radiance measured at ground by the National Research Councils Institute of Atmospheric Sciences and Climate have been compared with the MSG radiance measures. The joint use of ground and satellite radiance values led to the development of a Radiance Estimation Net (REN) algorithm yielding space-based estimates of the radiance on the field.