Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data
Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data
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DOI：
10.1016/j.jag.2015.02.005
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Rami Piiroinen;J. Heiskanen;M. Mõttus;P. Pellikka
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文献类型：
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作者：
Rami Piiroinen;J. Heiskanen;M. Mõttus;P. Pellikka
热带地区的土地利用方式正在快速变化。在撒哈拉以南非洲，森林、林地和灌木丛正在转变为农业用途，为迅速增长的人口生产粮食。本研究的目的是评估利用高空间和光谱分辨率aisaagle成像光谱数据在肯尼亚东南部高度异质研究区绘制常见农作物的前景。采用最小噪声分数变换将相干信息打包到更小的频带集合中，并采用支持向量机算法对数据进行分类。野外共测得35种植物，并以7种优势植物作为分类目标。其中五个目标是农作物。分类总体准确率（OA）为90.8%。为了评估剩余28种植物从分类结果中排除的可能性，使用SVM尝试了10种不同的概率阈值（PT）。利用所有35种已绘制的植物物种的验证多边形来评估PT的影响。结果表明，当PT值增加时，被排除在非目标多边形上的像元比被排除在7个分类目标多边形上的像元要多。这增加了OA，降低了分类结果中的盐和胡椒效应。非常高的空间分辨率图像和基于像素的分类方法可以很好地处理像玉米这样的小目标，而树冠的侧面则是混合的类别。
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.