Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images
dc.authorid | Alaa Ali Hameed / 0000-0002-8514-9255 | en_US |
dc.authorscopusid | Alaa Ali Hameed / 56338374100 | en_US |
dc.authorwosid | Alaa Ali Hameed / ABI-8417-2020 | |
dc.contributor.author | Khan, Aftab Ahmeda | |
dc.contributor.author | Jamil, Akhtarb | |
dc.contributor.author | Jamil A. | |
dc.contributor.author | Hussain, Dostdara | |
dc.contributor.author | Ali, Imrana | |
dc.contributor.author | Hameed, Alaa Ali | |
dc.date.accessioned | 2022-07-05T14:02:54Z | |
dc.date.available | 2022-07-05T14:02:54Z | |
dc.date.issued | 2022 | en_US |
dc.department | Ä°stinye Ãœniversitesi | en_US |
dc.description.abstract | In 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.citation | Khan, 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.2022.05.060 | en_US |
dc.identifier.doi | 10.1016/j.asr.2022.05.060 | en_US |
dc.identifier.issn | 0273-1177 | en_US |
dc.identifier.scopus | 2-s2.0-85131797739 | en_US |
dc.identifier.scopusquality | Q2 | en_US |
dc.identifier.uri | http://doi.org/10.1016/j.asr.2022.05.060 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12713/2961 | |
dc.identifier.wos | WOS:000991867900001 | en_US |
dc.identifier.wosquality | N/A | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.institutionauthor | Hameed, Alaa Ali | |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Science | en_US |
dc.relation.ispartof | Advances in Space Research | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Debris-Covered Glacie | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Generative Adversarial Network | en_US |
dc.subject | Sentinel-2 | en_US |
dc.title | Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images | en_US |
dc.type | Article | en_US |
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