Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images

dc.authoridAlaa Ali Hameed / 0000-0002-8514-9255en_US
dc.authorscopusidAlaa Ali Hameed / 56338374100en_US
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorKhan, Aftab Ahmeda
dc.contributor.authorJamil, Akhtarb
dc.contributor.authorJamil A.
dc.contributor.authorHussain, Dostdara
dc.contributor.authorAli, Imrana
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2022-07-05T14:02:54Z
dc.date.available2022-07-05T14:02:54Z
dc.date.issued2022en_US
dc.departmentÄ°stinye Ãœniversitesien_US
dc.description.abstractIn recent years, deep learning (DL) methods have proven their efficiency for various computer vision (CV) tasks such as image classification, natural language processing, and object detection. However, training a DL model is expensive in terms of both complex- ities of the network structure and the amount of labeled data needed. In addition, the imbalance among available labeled data for dif- ferent classes of interest may also adversely affect the model accuracy. This paper addresses these issues using a new convolutional neural network (CNN) based architecture. The proposed network incorporates both spatial and spectral information that combines two sub- networks: spatial-CNN and spectral-CNN. The spectral-CNN extracts spectral information, while spatial-CNN captures spatial infor- mation. Moreover, to make the features more robust, a multiscale spatial CNN architecture is introduced using different kernels. The final feature vector is formed by concatenating the outputs obtained from both spatial-CNN and spectral-CNN. To address the data imbalance problem, a generative adversarial network (GAN) was used to generate data for the underrepresented class. Finally, relatively a shallower network architecture was used to reduce the number of parameters in the network and improve the processing speed. The proposed model was trained and tested on Senitel-2 images for the classification of the debris-covered glacier. The results showed that the proposed method is well-suited for mapping and monitoring debris-covered glaciers at a large scale with high classification accuracy. In addition, we compared the proposed method with conventional machine learning approaches, support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP)en_US
dc.identifier.citationKhan, A. A., Jamil, A., Hussain, D., Ali, I., & Hameed, A. A. (2022). Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images. Advances in Space Research, doi:10.1016/j.asr.2022.05.060en_US
dc.identifier.doi10.1016/j.asr.2022.05.060en_US
dc.identifier.issn0273-1177en_US
dc.identifier.scopus2-s2.0-85131797739en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttp://doi.org/10.1016/j.asr.2022.05.060
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2961
dc.identifier.wosWOS:000991867900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorHameed, Alaa Ali
dc.language.isoenen_US
dc.publisherElsevier Scienceen_US
dc.relation.ispartofAdvances in Space Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDebris-Covered Glacieen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectMachine Learningen_US
dc.subjectGenerative Adversarial Networken_US
dc.subjectSentinel-2en_US
dc.titleDeep learning-based framework for monitoring of debris-covered glacier from remotely sensed imagesen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
Ä°sim:
1-s2.0-S0273117722004410-main.pdf
Boyut:
2.97 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
Ä°sim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama: