Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data

Remote Sensing

Saurabh Kaushik¹  Tejpal Singh¹  Andreas J. Dietz²  Pawan K. Joshi³  Anshuman Bhardwaj⁴ 

[1] Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India;German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Str. 20, 82234 Wessling, Germany;School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India;School of Geosciences, University of Aberdeen, Meston Building, King’s College, Aberdeen AB24 3UE, UK;

关键词: debris cover glacier;    deep neural network;    semantic segmentation;    remote sensing;    SAR;    Himalaya;   

DOI  :  10.3390/rs14061352

来源: DOAJ

【 摘 要 】

High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km² and 1710 km² of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.

【 授权许可】

Unknown