DOI: 10.1016/j.jag.2012.10.007
Scopus记录号: 2-s2.0-84880268563
论文题名: Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity
作者: Paneque-Gálvez J ; , Mas J ; -F ; , Moré G ; , Cristóbal J ; , Orta-Martínez M ; , Luz A ; C ; , Guèze M ; , Macía M ; J ; , Reyes-García V
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 23, 期: 1 起始页码: 372
结束页码: 383
语种: 英语
英文关键词: Bolivian Amazon
; Hybrid classification
; k-Nearest neighbor
; Remote sensing
; SVM
; Texture
; Thematic classification comparison
Scopus关键词: accuracy assessment
; algorithm
; artificial intelligence
; land classification
; land cover
; land use change
; Landsat
; landscape
; maximum likelihood analysis
; nearest neighbor analysis
; remote sensing
; spatial resolution
; Amazonia
; Bolivia
英文摘要: Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines - SVM), and hybrid (unsupervised-supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different depending on land use/cover classes. Early-growth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land use/cover classes were mapped with producer's and user's accuracies of ~90%. Our classification approach seems very well suited to accurately map land use/cover of heterogeneous landscapes, thus having great potential to contribute to climate change mitigation schemes, conservation initiatives, and the design of management plans and rural development policies. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79834
Appears in Collections: 气候变化事实与影响
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作者单位: Institut de Ciència i Tecnologia Ambientals (ICTA), Universitat Autònoma de Barcelona UAB), 08193 Bellatera, Barcelona, Spain; Centro de Investigaciones en Geografía Ambiental (CIGA), Universidad Nacional Autónoma de México (UNAM), Antigua Carretera a Pátzcuaro, No 8701, Col. Ex-Hacienda de San José de La Huerta, 58190 Morelia, Michoacán, Mexico; Centre de Recerca Ecològica i Aplicacions Forestals (CREAF), Universitat Autònoma de Barcelona (UAB), 08193 Bellatera, Barcelona, Spain; Departament de Biologia Animal, Biologia Vegetal i Ecologia, Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Barcelona, Spain; Departamento de Biología, Unidad de Botánica, Universidad Autónoma de Madrid (UAM), Calle Darwin 2, 28049 Madrid, Spain; ICREA and Institut de Ciència i Tecnologia Ambientals (ICTA), Universitat Autònoma de Barcelona (UAB), 08193 Bellatera, Barcelona, Spain
Recommended Citation:
Paneque-Gálvez J,, Mas J,-F,et al. Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,23(1)