Assessment of hotspots using sparse autoencoder in industrial zones

Basit Öğe Kaydı
dc.authoridErenoğlu, Ramazan Cüneyt / 0000-0002-8212-8379
dc.contributor.authorArslan, Enis
dc.contributor.authorErenoğlu, Ramazan Cüneyt
dc.date.accessioned2025-01-27T20:41:20Z
dc.date.available2025-01-27T20:41:20Z
dc.date.issued2019
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractRemote sensing satellite systems can be used to detect industrial zones by means of thermal infrared bands. There are several satellite systems loaded with thermal infrared sensors such as Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). In this study, ASTER thermal infrared data were converted to land surface temperature (LST) in order to determine hotspots caused by industrial zones. High LST values surrounded by low LST values are called hotspots here. These hotspots can be determined by applying different methodologies. One of these methods of sparse autoencoder can be used to indicate hotspots using different sizes of hidden layers. The principle of sparse autoencoder depends on unlabeled data in unsupervised learning. It does not need any information about labeled data as in supervised learning. The autoencoder reproduces its output with the same dimensions as the input image by managing the size of the hidden layer. The reconstruction of the image depends on the minimization of a cost function. The size of the hidden layer sets the fitting degree of the function for the reproduced image. A low-order reproduced image is the main target for hotspot detection. In this study, the difference between the original image and the reproduced image was analyzed for hotspot detection. Sparse autoencoder was successfully applied to ASTER thermal band 10 for hotspot detection in 7 pre-defined sites of a region known for steel industry for the two different days.
dc.identifier.doi10.1007/s10661-019-7572-3
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue7
dc.identifier.pmid31222399
dc.identifier.scopus2-s2.0-85067805099
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10661-019-7572-3
dc.identifier.urihttps://hdl.handle.net/20.500.12428/24112
dc.identifier.volume191
dc.identifier.wosWOS:000472494400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectRemote sensing
dc.subjectLand surface temperature
dc.subjectHotspot
dc.subjectAutoencoder
dc.titleAssessment of hotspots using sparse autoencoder in industrial zones
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Ramazan Cuneyt Erenoglu_Makale.pdf
Boyut:
10.91 MB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Harita Mühendisliği Bölümü Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu