Classification of Hyperspectral Images Using 3D CNN Based ResNet50
| dc.contributor.author | Firat, Huseyin | |
| dc.contributor.author | Hanbay, Davut | |
| dc.date.accessioned | 2024-04-24T17:11:23Z | |
| dc.date.available | 2024-04-24T17:11:23Z | |
| dc.date.issued | 2021 | |
| dc.department | Dicle Üniversitesi | en_US |
| dc.description | 29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK | en_US |
| dc.description.abstract | Hyperspectral images are images containing rich spectral and spatial information widely used in remote sensing applications. The development of deep learning techniques has had a significant impact on the classification of hyperspectral images. Different Convolutional Neural Network architectures have been used in many hyperspectral image analysis studies. However, the high dimensions of the hyperspectral images increased the computational complexity. For this reason, dimensionality reduction has been used in the preprocessing stage in many studies. Another difficulty encountered in hyperspectral image classification studies is the need to consider both spectral and spatial features. When deep spatial and spectral features are to be extracted, problems such as loss of gradient properties and degradation due to increased depth arise. In this study, the 3D convolutional neural network (CNN) based ResNet50 method is proposed to solve these problems encountered in hyperspectral studies and to extract sufficient spatial spectral properties from the network. Principal Component Analysis (PCA) was used to reduce spectral band excess. The proposed method has been applied to Pavia University and Salinas data sets. Overall accuracy, average accuracy and kappa values were used to measure the performance of the method. Calculated overall accuracy, average accuracy, and kappa values are 99.99% for the Pavia University data set, and while the overall accuracy and kappa values were 99.99% for the Salinas data set, the average accuracy value was 99.98%. | en_US |
| dc.description.sponsorship | IEEE,IEEE Turkey Sect | en_US |
| dc.identifier.doi | 10.1109/SIU53274.2021.9477899 | |
| dc.identifier.isbn | 978-1-6654-3649-6 | |
| dc.identifier.scopus | 2-s2.0-85111420648 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://doi.org/10.1109/SIU53274.2021.9477899 | |
| dc.identifier.uri | https://hdl.handle.net/11468/17461 | |
| dc.identifier.wos | WOS:000808100700141 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | tr | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 29th Ieee Conference on Signal Processing and Communications Applications (Siu 2021) | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | 3d Cnn | en_US |
| dc.subject | Resnet50 | en_US |
| dc.subject | Hyperspectral Image Classification | en_US |
| dc.subject | Principal Component Analysis | en_US |
| dc.subject | Deep Learning | en_US |
| dc.title | Classification of Hyperspectral Images Using 3D CNN Based ResNet50 | en_US |
| dc.title | Classification of Hyperspectral Images Using 3D CNN Based ResNet50 | |
| dc.type | Conference Object | en_US |
