气候变化领域集成服务门户(CCPortal): SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data

CCPortal

DOI	10.1016/j.jag.2018.11.011
	SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data
	Gonçalves J.; Pôças I.; Marcos B.; Mücher C.A.; Honrado J.P.
发表日期	2019
ISSN	15698432
起始页码	218
结束页码	230
卷号	76
英文摘要	Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods. Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest. We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03 – 0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10 – 20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective. Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the SegOptim approach (and potential solutions) as well as a future roadmap to expand its current functionalities. © 2018 Elsevier B.V.
英文关键词	Genetic algorithms; GEOBIA; Geographic object-based image analysis; High-spatial resolution; Image segmentation; Open-source software; Optimization; R package; Supervised classification
语种	英语
scopus关键词	genetic algorithm; image analysis; optimization; remote sensing; segmentation; software; spatial resolution; supervised classification
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156527
作者单位	CIBIO – Research Center in Biodiversity and Genetic Resources, InBIO – Research Network in Biodiversity and Evolutionary Biology, Associate Laboratory, Universidade do Porto, Campus Agrário de Vairão, Vairão4485-601, Portugal; Faculty of Sciences, University of Porto (FCUP), Edifício FC4 (Biologia), Rua do Campo Alegre, s/n, Porto, 4169-007, Portugal; Linking Landscape, Environment, Agriculture and Food, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, 1349-017, Portugal; Geo-Space Sciences Research Centre, Universidade do Porto, Porto, 4169-007, Portugal; Wageningen Environmental Research (Alterra), Earth Informatics subdivision, Wageningen University, Droevendaalsesteeg, Wageningen, 3 6708PB, Netherlands
推荐引用方式 GB/T 7714	Gonçalves J.,Pôças I.,Marcos B.,等. SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data[J],2019,76.
APA	Gonçalves J.,Pôças I.,Marcos B.,Mücher C.A.,&Honrado J.P..(2019).SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data.International Journal of Applied Earth Observation and Geoinformation,76.
MLA	Gonçalves J.,et al."SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data".International Journal of Applied Earth Observation and Geoinformation 76(2019).