This study aims to identify asbestos-containing materials (ACM) through the use of innovative technology such as aerial hyperspectral sensors. The development of operational methodologies and ad hoc processing were also applied for the purpose of this study. The activity was part of the ICT Living Labs DroMEP project carried out by Water Research Institute of the National Research Council (IRSA-CNR) and Servizi di Informazione Territoriale S.r.l. (SIT Srl). This was funded by the Apulia Region to support the growth and development of specialized SMEs in offering digital content and services. Uncontrolled abandoned wastes pose a threat to the human health and ecosystem. The presence of harmful or dangerous substances released without any control can become a dangerous source of pollution. Many areas of the Apulia region generally, in southern Italy, are subjected to this type of phenomena because most often, these areas are not easily accessible to Authorities for the control and management of the territory. Land monitoring and characterization operations would be carried out in a very long time and would require significant financial resources and considerable effort if done by conventional methods. The project activities include the testing and integration of several technologies already available, but not engineered for specific purposes. The work has been focused on the development of a methodology with a defined and high reliability capable of identifying the presence of ACM in various piles of rubbish abandoned in agro-ecosystems. The developed methodology analyses the spectral behaviour of ACM highlighting and emphasizing certain features through the use of a procedure based on an if-then-else control structure. It also allows the selection of the most effective features to combine that significantly reduces the number of false positives.
Detection of asbestos-containing materials in agro-ecosystem by the use of airborne hyperspectral CASI-1500 sensor including the limited use of two UAVs equipped with RGB cameras
Carmine Massarelli;Raffaella Matarrese;Vito Felice Uricchio;
2016
Abstract
This study aims to identify asbestos-containing materials (ACM) through the use of innovative technology such as aerial hyperspectral sensors. The development of operational methodologies and ad hoc processing were also applied for the purpose of this study. The activity was part of the ICT Living Labs DroMEP project carried out by Water Research Institute of the National Research Council (IRSA-CNR) and Servizi di Informazione Territoriale S.r.l. (SIT Srl). This was funded by the Apulia Region to support the growth and development of specialized SMEs in offering digital content and services. Uncontrolled abandoned wastes pose a threat to the human health and ecosystem. The presence of harmful or dangerous substances released without any control can become a dangerous source of pollution. Many areas of the Apulia region generally, in southern Italy, are subjected to this type of phenomena because most often, these areas are not easily accessible to Authorities for the control and management of the territory. Land monitoring and characterization operations would be carried out in a very long time and would require significant financial resources and considerable effort if done by conventional methods. The project activities include the testing and integration of several technologies already available, but not engineered for specific purposes. The work has been focused on the development of a methodology with a defined and high reliability capable of identifying the presence of ACM in various piles of rubbish abandoned in agro-ecosystems. The developed methodology analyses the spectral behaviour of ACM highlighting and emphasizing certain features through the use of a procedure based on an if-then-else control structure. It also allows the selection of the most effective features to combine that significantly reduces the number of false positives.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
