Segmentation of dust storm areas on Mars images using principal component analysis and neural network
Segmentation of dust storm areas on Mars images using principal component analysis and neural network
复制标题
DOI：
10.1186/s40645-019-0266-1
复制
发表时间：
2019-02-14
影响因子：
3.9
通讯作者：
Ogohara, Kazunori
中科院分区：
文献类型：
--
作者：
Gichu, Ryusei;Ogohara, Kazunori
我们提出了一种方法，自动分割的沙尘暴地区的火星轨道器观测到的图像。我们把它们分成小块。通过主成分分析，从许多小块中获得法向基向量。我们使用这些基向量的系数作为特征向量来训练分类器。测试图像中的所有块被分类器分类为沙尘暴，云和表面类之一。每个像元可能包含在几个沙尘暴斑块中。基于包括像素的沙尘暴斑块的数量，将像素分类为沙尘暴或其他类别。我们通过受试者操作特征曲线和曲线下面积（AUC）来评估分割方法。如果我们的目视检查确定的沙尘暴面积被假定为地面实况，则沙尘暴的AUC为0.947-0.978。如果我们有效地去除假阴性像素并使用两个不同的阈值保持真阳性沙尘暴的大小，则沙尘暴的精确度，召回率和F-测量分别为0.88，0.84和0.86。在这项研究中使用的分类器的调谐参数的确定，使沙尘暴的准确性最大化。我们还可以通过改变参数来调整云分割的分类器。
We present a method for automated segmentation of dust storm areas on Mars images observed by an orbiter. We divide them into small patches. Normal basis vectors are obtained from the many small patches by principal component analysis. We train a classifier using coefficients of these basis vectors as feature vectors. All patches in test images are categorized into one of the dust storm, cloud, and surface classes by the classifier. Each pixel may be included in several dust storm patches. The pixel is classified as a dust storm or the other classes based on the number of dust storm patches that include the pixel. We evaluate the segmentation method by the receiver operator characteristic curve and the area under the curve (AUC). AUC for dust storm is 0.947-0.978 if dust storm areas determined by our visual inspection are assumed to be ground truth. Precision, recall, and F-measure for dust storm are 0.88, 0.84, and 0.86, respectively, if we remove false negative pixels efficiently and maintain the size of true positive dust storms using two different threshold values. The tuning parameters of the classifier used in this study are determined so that the accuracy for dust storm is maximized. We can also tune the classifier for cloud segmentation by changing the parameters.