Combining Landsat and ALOS data for land cover mapping [Landsat ve ALOS Verilerini Kullanarak Arazi Örtüsü Haritasinin Oluşturulmasi]

In this study, L-band ALOS PALSAR radar satellite image and Landsat TM optical satellite image were used to investigate the contribution of radar satellite image to optical satellite image for land cover mapping. Dual-polarimetric data of ALOS satellite and also normalized difference vegetation index (NDVl) generated from Landsat image were used for the analysis. In addition, different classification techniques were taken into consideration and forest dominated land cover maps were produced and the results were compared. Random Forest (RF), k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) approaches were applied as image classification techniques. While the best result among the methods is DVM, the data set in which combined data are used gives the best general accuracy result. © 2017 IEEE.

Yazar Abdikan S.
Ustuner M.
Sanli F.B.
Bilgin G.
Yayın Türü Conference Object
Tek Biçim Adres https://hdl.handle.net/20.500.12628/4754
Tek Biçim Adres 10.1109/SIU.2017.7960379
Konu Başlıkları ALOS
image classification
land cover
Landsat
Koleksiyonlar Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD
Scopus İndeksli Yayınlar Koleksiyonu
Dergi Adı 2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Sayfalar -
Yayın Yılı 2017
Eser Adı
[dc.title]
Combining Landsat and ALOS data for land cover mapping [Landsat ve ALOS Verilerini Kullanarak Arazi Örtüsü Haritasinin Oluşturulmasi]
Yayıncı
[dc.publisher]
Institute of Electrical and Electronics Engineers Inc.
Yayın Türü
[dc.type]
conferenceObject
Açıklama
[dc.description]
25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703
Özet
[dc.description.abstract]
In this study, L-band ALOS PALSAR radar satellite image and Landsat TM optical satellite image were used to investigate the contribution of radar satellite image to optical satellite image for land cover mapping. Dual-polarimetric data of ALOS satellite and also normalized difference vegetation index (NDVl) generated from Landsat image were used for the analysis. In addition, different classification techniques were taken into consideration and forest dominated land cover maps were produced and the results were compared. Random Forest (RF), k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) approaches were applied as image classification techniques. While the best result among the methods is DVM, the data set in which combined data are used gives the best general accuracy result. © 2017 IEEE.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Yılı
[dc.date.issued]
2017
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/SIU.2017.7960379
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/4754
Yayın Dili
[dc.language.iso]
tur
Konu Başlıkları
[dc.subject]
ALOS
Konu Başlıkları
[dc.subject]
image classification
Konu Başlıkları
[dc.subject]
land cover
Konu Başlıkları
[dc.subject]
Landsat
Yazar
[dc.contributor.author]
Abdikan S.
Yazar
[dc.contributor.author]
Ustuner M.
Yazar
[dc.contributor.author]
Sanli F.B.
Yazar
[dc.contributor.author]
Bilgin G.
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Yazar Departmanı
[dc.contributor.department]
Zonguldak Bülent Ecevit Üniversitesi
Dergi Adı
[dc.relation.journal]
2017 25th Signal Processing and Communications Applications Conference, SIU 2017
ISBN
[dc.identifier.isbn]
9781509064946
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
11
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
11 Şubat 2024 15:41
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Tıklayınız
satellite classification techniques result optical Landsat Machines Vector Support approaches applied (k-NN) Neighbors methods combined general accuracy k-Nearest Forest (NDVl) L-band PALSAR investigate contribution mapping Dual-polarimetric normalized difference vegetation generated Random analysis addition different consideration
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