2014
DOI: 10.30638/eemj.2014.323
|View full text |Cite
|
Sign up to set email alerts
|

Testing Algorithms for the Identification of Asbestos Roofing Based on Hyperspectral Data

Abstract: There are several environmental issues in urban areas that are caused by the unintentional consequences of past activities. One of these issues is the wide application of asbestos cement in roofing materials in the 2 nd half of the 1900s. In this study, our goal was to identify different roof types and to determine those with asbestos components using high-ground (1 m) and spectral (126 bands) resolution airborne hyperspectral imagery (AISA Eagle II) and several classification approaches. In addition, we aimed… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

1
35
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
10

Relationship

0
10

Authors

Journals

citations
Cited by 29 publications
(36 citation statements)
references
References 20 publications
1
35
0
Order By: Relevance
“…Remote sensing can help in overcoming these omissions by providing maps of AC used as roofing material. Aerial remote sensing techniques have been successfully applied in the recent past for asbestos mapping purposes [7][8][9][10][11]. To the best of our knowledge, only one study [12] deals with the assessment of AC deterioration through spectral diagnostic bands.…”
Section: Introductionmentioning
confidence: 99%
“…Remote sensing can help in overcoming these omissions by providing maps of AC used as roofing material. Aerial remote sensing techniques have been successfully applied in the recent past for asbestos mapping purposes [7][8][9][10][11]. To the best of our knowledge, only one study [12] deals with the assessment of AC deterioration through spectral diagnostic bands.…”
Section: Introductionmentioning
confidence: 99%
“…Buildings can be discriminated from roads and pavements if we can assign a minimum height: a 3 m relative height combined with a vegetation index (e.g. normalized difference vegetation index, NDVI) gained from optical imaging is a clear difference (Abriha et al, 2018;Szabó et al, 2014). This method also helps to distinguish the low vegetation (i.e.…”
Section: Introductionmentioning
confidence: 99%
“…There have been several surveys undertaken on asbestos identification with the use of hyperspectral remote sensing data for a certain region, part of the country or city, in order to estimate the quantity of asbestos products in the study area (Fiumi et al 2012;Giannini et al 2012;Frassy et al 2014;Szabo et al 2014).…”
Section: Of the Council Of Ministers)mentioning
confidence: 99%