气候变化领域集成服务门户(CCPortal): Automated detection of rock glaciers using deep learning and object-based image analysis

CCPortal

DOI	10.1016/j.rse.2020.112033
	Automated detection of rock glaciers using deep learning and object-based image analysis
	Robson B.A.; Bolch T.; MacDonell S.; Hölbling D.; Rastner P.; Schaffer N.
发表日期	2020
ISSN	00344257
卷号	250
英文摘要	Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost. While the significance of rock glacier contribution to streamflow remains uncertain, the contribution is likely to be important for certain parts of the world. High-resolution remote sensing data has permitted the creation of rock glacier inventories for large regions. However, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation, which is both time consuming and subjective. Here, we present a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 optical imagery (10 m spatial resolution), Sentinel-1 interferometric coherence data, and a digital elevation model (DEM). CNNs identify recurring patterns and textures and produce a prediction raster, or heatmap where each pixel indicates the probability that it belongs to a certain class (i.e. rock glacier) or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment in the central Himalaya. In total, our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28%. Individual rock glacier polygons howevercontained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user's accuracy to be moderate (63.9–68.9%) even if the producer's accuracy was higher (75.0–75.4%). We repeated our method on very-high-resolution Pléiades satellite imagery and a corresponding DEM (at 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference image resolution makes. We found that working at a higher spatial resolution has little influence on the producer's accuracy (an increase of 1.0%), however the rock glaciers delineated were mapped with a greater user's accuracy (increase by 9.1% to 72.0%). By running all the processing within an object-based environment it was possible to both generate the deep learning heatmap and perform post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories. © 2020 The Author(s)
语种	英语
scopus关键词	Catchments; Classification (of information); Convolutional neural networks; Image resolution; Image segmentation; Learning systems; Mapping; Object detection; Remote sensing; Rocks; Runoff; Satellite imagery; Surveying; Textures; Digital elevation model; High resolution remote sensing; Interferometric coherence; Object based image analysis; Object based image analysis (OBIA); Spatial characteristics; Surrounding materials; Very high resolution; Deep learning; cryosphere; detection method; digital elevation model; image analysis; permafrost; rock glacier; satellite imagery; Sentinel; streamflow; Andes; Chile; Himalayas; La Laguna
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179148
作者单位	Department of Geography, University of Bergen, Norway; School of Geography and Sustainable Development, University of St. Andrews, United Kingdom; Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile; Department of Geoinformatics – Z_GIS, University of Salzburg, Austria; Department of Geography, University of Zurich, Switzerland
推荐引用方式 GB/T 7714	Robson B.A.,Bolch T.,MacDonell S.,et al. Automated detection of rock glaciers using deep learning and object-based image analysis[J],2020,250.
APA	Robson B.A.,Bolch T.,MacDonell S.,Hölbling D.,Rastner P.,&Schaffer N..(2020).Automated detection of rock glaciers using deep learning and object-based image analysis.Remote Sensing of Environment,250.
MLA	Robson B.A.,et al."Automated detection of rock glaciers using deep learning and object-based image analysis".Remote Sensing of Environment 250(2020).