A systematic extraction of glacial lakes for satellite imagery using deep learning based technique

For outburst susceptibility assessment of glacial lakes, the accurate extraction of glacial lakes region from satellite image is essential. Several techniques are introduced to extract glacial lakes over the years, such as based on image pixel or different thresholds and object-based statistics. These methods require manual optimization parameters for accurate extraction of the glacial lake's region. Hence, in this work, the deep learning (DL) models are utilized to segment the water and non-water bodies from satellite imagery. DL techniques are successful in many research areas for classification problems, but these techniques are relatively new for the extraction of glacial lakes. The main challenges in implementing DL techniques for the extraction of the glacial lakes are (i) preserving the semantic features of the image without loss during max-pooling operation, (ii) huge dataset requirement for proper training. To overcome the problems mentioned above, a customized architecture of the U-Net named as glacial lakes U-Net (GLU-Net) with various encoders is proposed in this work for effective segmentation of the glacial lakes. The proposed method for segmentation is also compared with the existing DL techniques in this work. The DL models are evaluated on Imja, Chandra, and Bhaga glacier regions datasets collected from Landsat 8 satellite imagery. The average sensitivity (Sen), specificity (Spe), and dice coefficient (Dice) for GLU-Net are 92.23%, 99.81%, and 93.46%. These are the three measures for an average of Imja, Chandra and Bhaga glacier regions. The qualitative and quantitative performance analysis shows the significant improvement of the proposed technique compared to other DL techniques for extraction of glacial lake's region.