MS-VACSNet: A Network for Multi-scale Volcanic Ash Cloud Segmentation in Remote Sensing Images

Abstract

The segmentation of volcanic ash clouds in remote sensing images provides valuable insights to study the volcanic deformation, forecasting, tracking, and hazard monitoring. However, the task of delineating the boundary of volcanic eruptions becomes difficult due to non-uniformity in the scale of eruptions across remote sensing images. In this paper, we propose a network for multi-scale volcanic ash clouds segmentation (MS-VACSNet) in remote sensing images. The proposed MS-VACSNet uses U-Net as base line with few improvements in the encoder and decoder sub-networks. Specifically, we employ dilated convolutions to capture the contextual information while delineating volcanic eruptions of different scales. We have conducted experiments on 10 active volcanic regions across the globe using MODIS thermal and infrared images. The experimental results show that our MS-VACSNet achieves an improvement of 5% in dice score compared to state-of-the-art segmentation approaches in segmenting the volcanic ash clouds.