In this paper the potential of the Hyperion spaceborne hyperspectral data in discriminating land covers in complex
natural ecosystems was evaluated according to the hierarchical structure of the European standard legend (CORINE
Land Cover 2000). Furthermore, the ability of the Hyperion data in retrieving land cover information at sub-pixel level
was assessed by exploiting the vegetation classes' distribution as obtained by aerial-photos.
Four standard supervised classifiers have been compared in terms of algorithm performance and class accuracy by
applying statistical metric; the best results were achieved with the Minimum Distance (MD) classifier.
In those areas exhibiting mixed pixels at the Hyperion spatial resolution a Linear Spectral Unmixing technique was
applied for deriving abundance fractions of the endmembers (i.e. land covers) previously identified. Accuracy of the un-mixing
analysis was evaluated using a Residual Error index calculated by relating Hyperion fractional abundances and
Results show the capability of Hyperion data to map land covers and vegetation diversity even at sub-pixel level within a
complex natural landscape.
Buried man-made structures, like archaeological handiworks, altering the natural trend of the soil surface can yield tonal
anomalies on remotely sensed images. These anomalies differ in size and/or intensity according to either the
environmental conditions at the time of acquisition or the spectral and spatial characteristics of the images. The research
challenge is to identify the best wavelength to detect these anomalies.
In this paper we have set up two new parameters for identifying and assessing the potential of anomaly detection: the
Detection Index (DI), which counts the pixels related to the marks, and the Separation Index (SI), which relates the
difference in brightness of the marks with respect to the background. These two indexes have been tested on MIVIS
(Multispectral Visible Imaging Spectrometer) airborne hyperspectral data acquired on remains not yet excavated of a few
archaeological sites. Results show that such indexes are an efficient, flexible and quick tool for assessing the image
potential to detect buried structures. Moreover, when they are applied to hyperspectral data, they allows for identifying
the spectral range more sensitive to the detection of the buried structures.
Aim of this study is the identification of the hyperspectral scanner operational characteristics allowing for asbestos
cement (AC) roofing sheets deterioration status assessment that is related to the asbestos fibers abundance.
At this purpose we made laboratory measurements on AC samples with different deterioration status collected in two
industrial areas in Italy. The asbestos occurrence in the AC samples was recognized using XRD and FTIR instruments
and the abundance of surfacing asbestos fibers was performed by using a high resolution scanner (SEM).
The samples optical characteristics and the directional effects that can affect the AC samples were analyzed using a
portable field spectrometer (ASD). The results of the ASD measurements (i.e. band-depth ratio of the continuum
removed calculated for the asbestos diagnostic band at 2.32μm) were related to the relative percentage of surfacing
asbestos fibers (i.e. the AC deterioration status).
Since laboratory measurements confirmed that optical measurements are sensitive to variations in asbestos fiber
abundance, detection limit analysis was used for defining the requirements (signal-to-noise ratio, band FWHM, and
sampling range) of an optimal hyperspectral sensor most suitable for detecting the diagnostic asbestos absorption