Study on the effect of training samples on the accuracy of crop remote sensing classification
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摘要: 为了比较好地研究和分析训练样本的数量和质量对农作物分类精度的影响，选取黑龙江省海伦市作为研究所需要的实验区，以Landsat 8遥感影像作为数据源，利用最大似然、神经网络、支持向量机3种分类方法分别去研究训练样本数量与质量对分类精度的影响，并且对3种分类方法进行了多次实验。最终的研究结果表明：（1）在训练样本质量相对恒定下，同一种分类方法对相同数量的训练样本的响应程度以及不同分类方法对训练样本数量的响应程度是不同的，并且分类精度存在不同程度的波动，随着训练样本数量的增加，这种波动会减小，当训练样本的数量达到一定程度，分类精度的均值将趋于相对稳定；（2）在训练样本数量恒定下，同一种分类方法以及不同种分类方法对相同质量等级的训练样本的响应程度是不同的；同一种分类方法对不同质量等级的训练样本响应程度也是不同的。Abstract: In order to study and analyse the influence the number of and quality of the training samples on the classification accuracy better, Helen city in Heilongjiang Province was chosen as the research required experimentation area, using Landsat 8 remote sensing images as the data source, the effects of the number and quantity of training samples on the classification accuracy were studied respectively by using the maximum likelihood, neural network and support vector machine three kinds of methods, and several experiments were made on these three kinds of classification methods. The final result shows that:(1) when the training sample quality is relatively constant, the degree of response of the same classification method to the same number of training samples as well as the degree of response of the different classification methods to the number of training samples are different, and the classification accuracy has different degree of volatility, with the increase of the number of training samples, the volatility will decrease, when the number of training samples reaches a certain degree, the mean of classification accuracy will tend to be relatively stable; (2) when the number of training samples is constant, the same classification methods as well as the different classification methods have different degree of response to the training samples of the same quality grade; the degree of response of the same classification method to the different training samples quality level is also different.
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