Basically, remote sensing can be very useful in preparing an intensive survey campaign or directing fieldwork. In fact, viewing the archaeological structures from ground level generally does not clearly identify the spatial characteristics of these structures or the relationship to the surrounding archaeological sites. The basic assumption of image-interpretation for the recognition of the buried structures is that they can alter the natural trend of the superficial soil and vegetation growth and such alterations can develop into permanent surface spectral features [1,2]. These changes can mark out the pixel appearing with differences, with respect to the adjacent pixels, in color, texture, brightness or combination thereof [4].
The identification of these relevant anomalies, expected in presence of buried man-made structures, depends usually on the experience of the photo-interpreter and his knowledge of the territory [5]. However, environmental factors such as the compaction of soil, moisture content and vegetation impact the effectiveness of the technique to detect subsurface remains [2,5]. In this perspective, one of the challenging research aspects is not only to verify if the most advanced and very high spatial resolution satellite (e.g., IKONOS and QuickBird), or the airborne hyperspectral imagery (e.g., the AHS, the AHI, the CASI and the HyMAP hyperspectral sensors), are feasible for a visual interpretation [6], but it is to identify the image spectral characteristics that bear the highest inherent archaeological information content [7,8].
On the basis of the high spectral and spatial resolution offered by the remotely sensed hyperspectral data, the different spectral anomalies linked to the presence of subsurface archaeological structures should be highlighted by using specific spectral channels and/or their spectral combinations. Recent studies carried out by [9] highlighted the sensitivity of the airborne Multispectral Infrared Visible Imaging Spectrometer (MIVIS) imagery for the detection of surface anomalies linked to the presence of archaeological remains.In this framework, the paper analyzes Drug_discovery the spectral information of MIVIS sensor with respect to the dominant land cover surfacing buried archaeological structures (e.g.
, stone walls, floors, plaster or tile concentrations, packed earth, pavements near the surface) in 97 test sites (collected within five different archaeological areas in Italy) to assess the best wavelength bands useful for their detection.Starting from certain training information, i.e. using only those archeological areas where field campaigns and visual interpretation on MIVIS imagery were already performed by archaeologists on not yet excavated buried remnants, 97 pairs of Regions Of Interest (ROI) encompassing the spectral anomaly-background system related to the archaeological remains were manually delineated on MIVIS images.