Regional scale crop mapping using multi-temporal satellite imagery
Regional scale crop mapping using multi-temporal satellite imagery
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DOI：
10.5194/isprsarchives-xl-7-w3-45-2015
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
N. Kussul;S. Skakun;A. Shelestov;M. Lavreniuk;B. Yailymov;O. Kussul
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文献类型：
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作者：
N. Kussul;S. Skakun;A. Shelestov;M. Lavreniuk;B. Yailymov;O. Kussul
在处理大面积(超过10,000平方英尺)的光学图像时存在的问题之一。Km)是云和阴影的存在，这些云和阴影导致数据集中缺少值。提出了一种多时相光学卫星影像云、阴影缺失数据分类的新方法。首先，使用自组织Kohonen映射(SOM)来恢复卫星图像时间序列中缺失的像素值。使用非缺失值分别为每个光谱波段训练SOM。缺失值通过一个特殊的过程来恢复，该过程用神经元的权重系数替换输入样本的缺失分量。在缺失数据恢复后，对多时相卫星图像进行监督分类。提出了一种神经网络集成，特别是多层感知器(MLP)。神经网络的集成是通过平均委员会技术来完成的，即计算分类器上的平均类概率，并为给定的输入样本选择平均后验概率最高的类。所提出的方法被应用于2013年乌克兰JECAM试验场的多时相Landsat-8图像的区域尺度作物分类。结果表明，在总体分类精度、kappa系数以及生产者和用户对不同类别的分类精度方面，MLP集成比单个神经网络具有更好的性能。总体准确率达到85%以上。获得的分类图也通过估计作物面积和与官方统计数据的比较进行了验证。
One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.