Authors:
Giuseppe Bonifazi
1
;
Giuseppe Capobianco
1
;
Riccardo Gasbarrone
1
;
Silvia Serranti
1
;
Sergio Bellagamba
2
and
Daniele Taddei
1
Affiliations:
1
DICMA, Department of Chemical Engineering, Materials and Environment, Sapienza - University of Rome, via Eudossiana 18, 00184, Italy
;
2
INAIL - Italian Workers’ Compensation Authority, Research Division, DIT - Department for Technological Innovations and Security Equipment, Products and Human Settlements, via R. Ferruzzi 38/40, Rome, Italy
Keyword(s):
PRISMA, Imaging Spectroscopy, Classification, Asbestos, Asbestos-containing Materials (ACMs).
Abstract:
In the last few decades, the procedure for identifying, classifying and mapping the asbestos-containing materials (ACMs), and contaminated areas, is considered one of the most important aspects for the purpose of remediation. This task, carried out by skilled workers, can be very long and difficult to perform, and it can also increase the risk of inhalation of asbestos fibers. The identification and characterization of areas contaminated by asbestos using remote sensing techniques represent a valid alternative to census methods, traditionally based on visual inspection of surfaces and in situ sampling to be analyzed later in the laboratory. The aim of this work was to explore the possibilities of using machine learning techniques to identify possible asbestos-contaminated areas and ACMs by using PRISMA satellite imagery in areas where chrysotile was once extracted, processed and used in asbestos-containing products (ACPs). The study area is located in the Balangero’s asbestos mine si
te. More in detail, Principal Component Analysis (PCA) was performed on a Visible, Near-InfraRed and Short-Wave InfraRed (VNIR-SWIR) PRISMA image to reduce data dimensionality and used as an exploratory analysis tool. Classification And Regression Trees (CART) technique was finally utilized to test a classification of six predetermined classes on the panchromatic image.
(More)