ABSTRACT:The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this paper, we present the new construction of the hyperspectral imaging system, made of industrial hyperspectral line scanner ImSpector V9 and CCD-sensor PixelView. The hyperspectral line scanner is calibrated geometrically, and hyperspectral data are geocoded and converted to the hyperspectral cube. The system abilities are evaluated for various archaeological artefacts made of different materials. Our experience in applications, visualisations, and interpretations of collected hyperspectral data are explored and presented.

ABSTRACT:The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this paper, we present the new construction of the hyperspectral imaging system, made of industrial hyperspectral line scanner ImSpector V9 and CCD-sensor PixelView. The hyperspectral line scanner is calibrated geometrically, and hyperspectral data are geocoded and converted to the hyperspectral cube. The system abilities are evaluated for various archaeological artefacts made of different materials. Our experience in applications, visualisations, and interpretations of collected hyperspectral data are explored and presented.

This article describes the adaptation of an existing aerial hyperspectral imaging system in a low-cost setup for collecting hyperspectral data in laboratory and field environment and spatial distortion assessments. The imaging spectrometer system consists of an ImSpector V9 hyperspectral pushbroom scanner, PixelFly high performance digital CCD camera, and a subsystem for navigation, position determination and orientation of the system in space, a sensor bracket and control system. The main objective of the paper is to present the system, with all its limitations, and a spatial calibration method. The results of spatial calibration and calculation of modulation transfer function (MTF) are reported along with examples of images collected and potential uses in agronomy. The distortion value rises drastically at the edges of the image in the near-infrared segment, while the results of MTF calculation showed that the image sharpness was equal for the bands from the visible part of the spectrum, and approached Nyquist’s theory of digitalization. In the near-infrared part of the spectrum, the MTF values showed a less sharp decrease in comparison with the visible part. Preliminary image acquisition indicates that this hyperspectral system has potential in agronomic applications.

Development of remote sensing and increased availability of satellite imagery of different spatial, spectral, temporal and radiometric characteristics makes information obtained from such sources of vital importance for studying and mapping vegetation. Vegetation indices have a significant role in vegetation change detection and tracking, whether in quantity or quality terms. Each index has specific significance and performance characteristics. A multiple regression statistical analysis of average vegetation index values (NDVI, NDWI, GNDVI, EVI and SAVI) was performed for 2012, 2013 and 2014 periods of a wider Česma forest area near Vrbovec, Croatia. Further, rasters using three-year average values, sums, variances and standard deviations for all five indices were created. Differencing of average NDVI index values for years 2005 and 2014 was also performed. Imagery chosen was from the active vegetation period and used as a basis for cluster analysis detection of significant change areas. Česma forest area was selected due to previous field monitoring and point analysis conducted (2012, 2013 and 2014) that serve as validation for this research. Finally, a raster analysis of select areas, surrounding accumulation dam and encompassing Česma forest area exclusively, was conducted. The intention was determining vegetation index change dynamics. Spaciotemporal analysis around accumulation dams determined vegetation changes in dam areas. The advantage of the applied method is that, by using the Principal Components Analysis-PCA, it allows change detection, tracking and monitoring on wide areas more promptly than with other methods.The analysis itself was made using 92 LANDSAT images acquired over a 10-year period.