DOI: 10.1016/j.jag.2015.02.005
Scopus记录号: 2-s2.0-84930388733
论文题名: Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data
作者: Piiroinen R ; , Heiskanen J ; , Mõttus M ; , Pellikka P
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 39 起始页码: 1
结束页码: 8
语种: 英语
英文关键词: Heterogeneous landscape
; Imaging spectroscopy
; Land cover classification
; Minimum noise fraction
; Support vector machine
Scopus关键词: agricultural land
; classification
; crop
; land cover
; noise
; satellite data
; satellite imagery
; spectroscopy
; support vector machine
; Kenya
; Zea mays
英文摘要: Land use practices are changing at a fast pace in the tropics. In sub-Saharan Africa forests, woodlands and bushlands are being transformed for agricultural use to produce food for the rapidly growing population. The objective of this study was to assess the prospects of mapping the common agricultural crops in highly heterogeneous study area in south-eastern Kenya using high spatial and spectral resolution AisaEAGLE imaging spectroscopy data. Minimum noise fraction transformation was used to pack the coherent information in smaller set of bands and the data was classified with support vector machine (SVM) algorithm. A total of 35 plant species were mapped in the field and seven most dominant ones were used as classification targets. Five of the targets were agricultural crops. The overall accuracy (OA) for the classification was 90.8%. To assess the possibility of excluding the remaining 28 plant species from the classification results, 10 different probability thresholds (PT) were tried with SVM. The impact of PT was assessed with validation polygons of all 35 mapped plant species. The results showed that while PT was increased more pixels were excluded from non-target polygons than from the polygons of the seven classification targets. This increased the OA and reduced salt-and-pepper effects in the classification results. Very high spatial resolution imagery and pixel-based classification approach worked well with small targets such as maize while there was mixing of classes on the sides of the tree crowns. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79501
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, Finland
Recommended Citation:
Piiroinen R,, Heiskanen J,, Mõttus M,et al. Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,39