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

Khan, Aftab AhmedaJamil, AkhtarbJamil A.Hussain, DostdaraAli, ImranaHameed, Alaa Ali2022-07-052022-07-052022Khan, 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.2022.05.0600273-1177http://doi.org/10.1016/j.asr.2022.05.060https://hdl.handle.net/20.500.12713/2961In 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)eninfo:eu-repo/semantics/closedAccessDebris-Covered GlacieConvolutional Neural NetworkMachine LearningGenerative Adversarial NetworkSentinel-2Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed imagesArticleWOS:0009918679000012-s2.0-85131797739N/A10.1016/j.asr.2022.05.060Q2