Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Science
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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)
Açıklama
Anahtar Kelimeler
Debris-Covered Glacie, Convolutional Neural Network, Machine Learning, Generative Adversarial Network, Sentinel-2
Kaynak
Advances in Space Research
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
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
