水稻种植面积遥感估算的不确定性研究

利用研究区地物类别亚米级GPS详查数据及TM影像光谱数据,模拟生成1m分辨率的遥感模拟影像。用3种非参数分类法(最临近法KNN、误差后向传播神经网络BPN,模糊自适应网络FUZZY ARTMAP)和一种参数分类法(最大似然法MLC)对研究区TM影像进行硬分类估算水稻面积;还采用BPN全模糊分类、BPN和KNN模糊分类、抽象级结合和测量级结合的多分类器结合方法对遥感影像进行分类估算水稻面积;采用最多数法则的尺度扩展算法,实现由3m空间分辨率参考图提取30m空间分辨率影像像元纯度信息,讨论混合像元问题对遥感影像分类精度的影响。结果表明:非参数分类法精度均高于参数分类法,3种非参数分类法之间的差异较小,用最大似然法估算水稻面积的用户精度最高,用K最临近值分类法估算水稻面积的生产者精度最高;水稻类全模糊分类法的面积和真实面积最为接近,水稻类像元内的面积估测和真实面积无极显著差异;多分类器结合的分类法无论采用投票法还是测量级方法都能提高分类的总精度,能够提高水稻类面积提取的精度;研究区在30m空间分辨率的情况下,各类别分类总精度、Kappa系数随像元纯度升高而升高,4种硬分类方法没有对混合像元的分类表现出特别强的能力。本研究最终制作出分类影像像元的分类结果图、分类最大概率值、熵值图和水稻类概率值等4张图层,构成了对研究区分类结果不确定性的空间分布图不确定性图层,为采取进一步降低不确定性的措施提供了线索。

Uncertainty analysis of rice planting area extraction based on different classifiers using Landsat data

Abstract: Rice is the staple food for over half of the world's population and two-thirds of the population of China. One of the main methods to implement an estimate of the planting area is to classify an image of the study area. Systematic quality assessment and some quantitative researches have been made on uncertainties in rice area estimation using remote sensing data. In this paper, sub-meter GPS data from a field campaign and TM image of study area were combined to obtain 1m resolution sub-pixels of simulated images. Maximum Likelihood Classifier(MLC), K-Nearest Neighbors (KNN), BP neural network (BPN) and Fuzzy ARTMAP neural network (FUZZY ARTMAP) were used as hard classification approaches to classify the TM image of the study area. Classification results showed that the classification precision of all non-parametric approaches (KNN,BPN and FUZZY ARTMAP) were higher than that of parametric approach (MLC). The differences of overall accuracy between these three non-parameters classifications were small. As for rice area, it's better to choose MLC to get higher User's Accuracy, and choose KNN to get higher Producer's Accuracy. Full fuzzy BPN, partial fuzzy BPN and KNN classifiers were used to estimate area of classes in sub-pixels of simulated and TM images. The accuracies of area estimation by full fuzzy BPN classifier were significantly higher than these by partial fuzzy BPN and KNN classifiers. The correlation coefficient between the predicted area and true area of sub-pixels was not suitable in accuracy assessment for fuzzy classification, but a paired t-test could be used to assess well accuracy of area estimation. Full fuzzy classifiers have advantages of selecting eligible and enough training samples over partial fuzzy classifiers and enhance classification precision. But classification results failed to offer different categories of each pixel in space in the location information. The combined multiple classifiers either in voting mode or in measuring mode showed capacities to enhance the overall classification uncertainty in this study. It can help to improve the precision of the rice area extraction to some extent. An approach to analyzing the mixing degree of pixels was proposed in this study. The mixing degree of pixels of 30m resolution TM image was calculated by up scaling thematic map on majority rule in Matlab. As far as the condition of rice growing regions in southern China is concerned, the problem of mixed pixel is much more severe for commonly used images like TM images. And the classification results demonstrated that the classification precision decreased with the pureness of pixels and four classifiers showed no difference in capacity to classify mixing pixels. Based on Probability Vector which was available to BPN and KNN classifiers, the maps of maximum probability, entropy of all pixels and probability of pixels with rice label were made to represent uncertainties of classification for the TM image of the study area. These maps with the traditional classification map can transfer not only results of classification but also information of spatial variation of classification uncertainty to users.