干旱区生态环境知识资源中心(Arid): Automated detection of rock glaciers using deep learning and object-based image analysis

Arid

DOI	10.1016/j.rse.2020.112033
	Automated detection of rock glaciers using deep learning and object-based image analysis
	Robson, Benjamin Aubrey; Bolch, Tobias; MacDonell, Shelley; Hoelbling, Daniel; Rastner, Philipp; Schaffer, Nicole
通讯作者	Robson, BA
来源期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
EISSN	1879-0704
出版年	2020
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 Pleiades 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.
类型	Article
语种	英语
开放获取类型	hybrid, Green Published, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000585306100006
WOS关键词	DEBRIS-COVERED GLACIERS ; NORTHERN TIEN-SHAN ; CLIMATE-CHANGE ; DRY ANDES ; INVENTORY ; CHILE ; PERMAFROST ; SAR ; CLASSIFICATION ; SEGMENTATION
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/327288
作者单位	[Robson, Benjamin Aubrey] Univ Bergen, Dept Geog, Bergen, Norway; [Bolch, Tobias] Univ St Andrews, Sch Geog & Sustainable Dev, St Andrews, Fife, Scotland; [MacDonell, Shelley; Schaffer, Nicole] Ctr Estudios Avanzados Zonas Aridas CEAZA, La Serena, Chile; [Hoelbling, Daniel] Univ Salzburg, Dept Geoinformat Z GIS, Salzburg, Austria; [Rastner, Philipp] Univ Zurich, Dept Geog, Zurich, Switzerland
推荐引用方式 GB/T 7714	Robson, Benjamin Aubrey,Bolch, Tobias,MacDonell, Shelley,et al. Automated detection of rock glaciers using deep learning and object-based image analysis[J],2020,250.
APA	Robson, Benjamin Aubrey,Bolch, Tobias,MacDonell, Shelley,Hoelbling, Daniel,Rastner, Philipp,&Schaffer, Nicole.(2020).Automated detection of rock glaciers using deep learning and object-based image analysis.REMOTE SENSING OF ENVIRONMENT,250.
MLA	Robson, Benjamin Aubrey,et al."Automated detection of rock glaciers using deep learning and object-based image analysis".REMOTE SENSING OF ENVIRONMENT 250(2020).