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首页> 外文会议>International symposium on spatial accuracy assessment in natural resources and environmental science >Visualisation of Measures of Classifier Reliability and Error in Remote Sensing
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Visualisation of Measures of Classifier Reliability and Error in Remote Sensing

机译：遥感中分类器可靠性和误差的可视化

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The estimation of the accuracy of a thematic classification derived from remotely sensed data is generally based on the confusion or error matrix. This matrix is derived by evaluating the performance of the classifier on a set of test data, and the quantities derived from analysis of the confusion matrix include percent accuracy (total and per class), producer's accuracy, consumer's accuracy, and varieties of the kappa coefficient. None of these measures considers the spatial distribution of erroneously classified pixels, either implicitly or explicitly. Furthermore, each pixel in the image is assigned a unique ("hard") label but generally no measure of confidence is assigned to that label. In this paper we propose a methodology that specifically takes into account the spatial pattern of errors of omission and commission, and which presents the user with an indication of the reliability of pixel label assignments. Our methodology assumes that an accurate digital map of the spatial objects (fields, lakes, or forests) being classified, plus cultural features such as roads and urban areas, is available. Such a map can be obtained by digitising a large-scale paper map of the study area, or via image processing. Assuming also that the number of classes is appropriate to the problem at hand and to the scale of the image, it is likely that a substantial number of erroneous label allocations relate to mixed pixels located near or on field boundaries. A buffering operation is applied to the digitised boundary information in order to generate a mask. The width of the buffer is determined from a study of the location of the erroneously labelled pixels. To this mask are added regions not included in the classification, for example roads and urban areas. Furthermore, we look at the spatial distribution of the remaining errors to determine whether these errors are spatially random or clustered in their distribution. Such information can help to refine the classification. Finally, we present a method using colour coding that allows the visualisation of the reliability of the classifier output. The method can be applied to neural or statistical classifiers.
机译：估计从远程感测数据导出的主题分类的准确性通常基于混淆或误差矩阵。该矩阵是通过评估分类器对一组测试数据的性能而导出的，并且从混淆矩阵的分析导出的数量包括精度（总和每班），生产者的准确性，消费者的准确性和kappa系数的品种。 。这些措施均未考虑错误地或明确的错误分类像素的空间分布。此外，图像中的每个像素被分配了唯一（“硬”）标签，但通常不会分配给该标签的置信度。在本文中，我们提出了一种专门考虑了遗漏和委托错误的空间模式的方法，并呈现了用户指示像素标签分配的可靠性。我们的方法假设可提供分类的空间物体（田间，湖泊或森林）的准确数字地图，以及道路和城市地区等文化功能。通过数字化研究区域的大规模纸质地图或通过图像处理来获得这样的地图。此外，还假设类的数量适用于手头的问题和图像的比例，很可能是大量的错误标签分配涉及位于近乎或现场边界的混合像素。缓冲操作应用于数字化边界信息以便生成掩模。从错误标记像素的位置的研究确定缓冲器的宽度。对于该掩模，添加不包括在分类中的区域，例如道路和城市地区。此外，我们查看剩余误差的空间分布，以确定这些错误是否在其分发中是空间上随机或聚集的。此类信息可以有助于改进分类。最后，我们介绍了一种使用颜色编码的方法，其允许可视化分类器输出的可靠性。该方法可以应用于神经或统计分类器。

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