Vegetation cover analysis using a low budget hyperspectral proximal sensing system
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Abstract
This report describes the implementation of a hyperspectral proximal sensing low-budget acquisition system and
its application to the detection of terrestrian vegetation cover anomalies in sites of high environmental quality.
Anomalies can be due to stress for lack of water and/or pollution phenomena and weed presence in agricultural
fields. The hyperspectral cube (90-bands ranging from 450 to 900 nm) was acquired from the hill near Segni
(RM), approximately 500 m far from the target, by means of electronically tunable filters and 8 bit CCD cameras.
Spectral libraries were built using both endmember identification method and extraction of centroids of the
clusters obtained from a k-means analysis of the image itself. Two classification methods were applied on the
hyperspectral cube: Spectral Angle Mapper (hard) and Mixed Tuned Matching Filters (MTMF). Results show
the good capability of the system in detecting areas with an arboreal, shrub or leafage cover, distinguishing between
zones with different spectral response. Better results were obtained using spectral library originated by the
k-means method. The detected anomalies not correlated to seasonal phenomena suggest a ground true analysis
to identify their origin.
its application to the detection of terrestrian vegetation cover anomalies in sites of high environmental quality.
Anomalies can be due to stress for lack of water and/or pollution phenomena and weed presence in agricultural
fields. The hyperspectral cube (90-bands ranging from 450 to 900 nm) was acquired from the hill near Segni
(RM), approximately 500 m far from the target, by means of electronically tunable filters and 8 bit CCD cameras.
Spectral libraries were built using both endmember identification method and extraction of centroids of the
clusters obtained from a k-means analysis of the image itself. Two classification methods were applied on the
hyperspectral cube: Spectral Angle Mapper (hard) and Mixed Tuned Matching Filters (MTMF). Results show
the good capability of the system in detecting areas with an arboreal, shrub or leafage cover, distinguishing between
zones with different spectral response. Better results were obtained using spectral library originated by the
k-means method. The detected anomalies not correlated to seasonal phenomena suggest a ground true analysis
to identify their origin.
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How to Cite
(1)
Cenedese, A.; Miozzi, M.; Benetazzo, A.; Paglialunga, A.; Daquino, C.; Mussapi, R. Vegetation Cover Analysis Using a Low Budget Hyperspectral Proximal Sensing System. Ann. Geophys. 2006, 49 (1). https://doi.org/10.4401/ag-3169.