气候变化领域集成服务门户(CCPortal): A new supervised classifier exploiting spectral-spatial information in the Bayesian framework

CCPortal

DOI	10.1016/j.jag.2019.101990
	A new supervised classifier exploiting spectral-spatial information in the Bayesian framework
	Barca E.; Castrignanò A.; Ruggieri S.; Rinaldi M.
发表日期	2020
ISSN	15698432
卷号	86
英文摘要	Conventional machine learning methods are often unable to achieve high degrees of accuracy when only spectral data are involved in the classification process. The main reason of that inaccuracy can be brought back to the omission of the spatial information in the classification. The present paper suggests a way to combine effectively the spectral and the spatial information and improve the classification's accuracy. In practice, a Bayesian two-stage methodology is proposed embodying two enhancements: i) a geostatistical non-parametric classification approach, the universal indicator kriging and ii) the smooth multivariate kernel method. The former provides an informative prior, while the latter overcomes the assumption (often not true) of independence of the spectral data. The case study reports an application to land-cover classification in a study area located in the Apulia region (Southern Italy). The methodology performance in terms of overall accuracy was compared with five state-of-the-art methods, i.e. naïve Bayes, Random Forest, artificial neural networks, support vector machines and decision trees. It is shown that the proposed methodology outperforms all the compared methods and that even a severe reduction of the training set does not affect seriously the average accuracy of the presented method. © 2019 Elsevier B.V.
英文关键词	Bayes’ method; Land-cover classification; multivariate smooth kernel; universal indicator kriging
语种	英语
scopus关键词	accuracy assessment; artificial neural network; Bayesian analysis; GIS; image analysis; image classification; land cover; machine learning; spatial data; spectral analysis; Italy; Puglia
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/156432
作者单位	Water Research Institute of the Italian Research Council (IRSA-CNR), Bari, Italy; Council for Agricultural Research and Economics, Research Center for Agriculture and Environment (CREA-AA), Bari, Italy; Council for Agricultural Research and Economics, Research Centre forCereal and Industrial Crops(CREA-CI), Foggia, Italy
推荐引用方式 GB/T 7714	Barca E.,Castrignanò A.,Ruggieri S.,et al. A new supervised classifier exploiting spectral-spatial information in the Bayesian framework[J],2020,86.
APA	Barca E.,Castrignanò A.,Ruggieri S.,&Rinaldi M..(2020).A new supervised classifier exploiting spectral-spatial information in the Bayesian framework.International Journal of Applied Earth Observation and Geoinformation,86.
MLA	Barca E.,et al."A new supervised classifier exploiting spectral-spatial information in the Bayesian framework".International Journal of Applied Earth Observation and Geoinformation 86(2020).