冰川测绘的多传感器深度学习

Codruţ-Andrei Diaconu, Konrad Heidler, Jonathan L. Bamber, Harry Zekollari
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引用次数: 0

摘要

冰原之外的 20 多万个冰川对海平面上升、水资源管理、自然灾害、生物多样性和旅游业都有影响,在我们的社会中发挥着至关重要的作用。然而,只有一小部分冰川受益于持续、详细的现场观测,从而能够评估其状态和随时间的变化。基于卫星的冰川测绘应用历来主要依赖人工和半自动探测方法,而最近开始向深度学习技术快速而显著地过渡。本章回顾了多传感器遥感数据与深度学习的结合如何让我们更好地划分(即绘制)冰川并探测其时变。我们解释了依靠深度学习多传感器框架绘制冰川地图如何受益于区域和全球冰川清单的广泛可用性。我们还分析了冰川测绘背后的原理、深度学习方法的益处,以及将多传感器地球观测数据与深度学习算法相结合的固有挑战。虽然我们的综述旨在对冰川测绘工作进行广泛概述,但我们强调了深度学习多传感器遥感应用具有相当大潜在附加值的一些设置。这包括应用于从视觉上很难与周围环境区分开来的碎屑覆盖冰川和岩石冰川,以及与海洋接触的塌方冰川。这些具体案例通过一系列可视成像进行了说明,突出了在检测冰川变化时的一些重要优势和挑战,包括处理季节性积雪、不断变化的碎石覆盖以及区分冰川前沿和周围海冰。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Sensor Deep Learning for Glacier Mapping
The more than 200,000 glaciers outside the ice sheets play a crucial role in our society by influencing sea-level rise, water resource management, natural hazards, biodiversity, and tourism. However, only a fraction of these glaciers benefit from consistent and detailed in-situ observations that allow for assessing their status and changes over time. This limitation can, in part, be overcome by relying on satellite-based Earth Observation techniques. Satellite-based glacier mapping applications have historically mainly relied on manual and semi-automatic detection methods, while recently, a fast and notable transition to deep learning techniques has started. This chapter reviews how combining multi-sensor remote sensing data and deep learning allows us to better delineate (i.e. map) glaciers and detect their temporal changes. We explain how relying on deep learning multi-sensor frameworks to map glaciers benefits from the extensive availability of regional and global glacier inventories. We also analyse the rationale behind glacier mapping, the benefits of deep learning methodologies, and the inherent challenges in integrating multi-sensor earth observation data with deep learning algorithms. While our review aims to provide a broad overview of glacier mapping efforts, we highlight a few setups where deep learning multi-sensor remote sensing applications have a considerable potential added value. This includes applications for debris-covered and rock glaciers that are visually difficult to distinguish from surroundings and for calving glaciers that are in contact with the ocean. These specific cases are illustrated through a series of visual imageries, highlighting some significant advantages and challenges when detecting glacier changes, including dealing with seasonal snow cover, changing debris coverage, and distinguishing glacier fronts from the surrounding sea ice.
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