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An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification

机译：土地覆被分类平滑标注方法概述与比较

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摘要

An elementary piece of our prior knowledge about images of the physical world is that they are spatially smooth, in the sense that neighboring pixels are more likely to belong to the same object (class) than to different ones. The smoothness assumption becomes more important as sensor resolutions keep increasing, both because the radiometric variability within classes increases and because remote sensing is employed in more heterogeneous areas (e.g., cities), where shadow and shading effects, a multitude of materials, etc., degrade the measurement data, and prior knowledge plays a greater role. This paper gives a systematic overview of image classification methods, which impose a smoothness prior on the labels. Both local filtering-type approaches and global random field models developed in other fields of image processing are reviewed, and two new methods are proposed. Then follows a detailed experimental comparison and analysis of the presented methods, using two different aerial data sets from urban areas with known ground truth. A main message of the paper is that when classifying data of high spatial resolution, smoothness greatly improves the accuracy of the result—in our experiments up to 33%. A further finding is that global random field models outperform local filtering methods and should be more widely adopted for remote sensing. Finally, the evaluation confirms that all methods already oversmooth when most effective, pointing out that there is a need to include more and more complex prior information into the classification process.
机译：我们关于物理世界图像的先验知识的一个基本方面是，它们在空间上是平滑的，从某种意义上说，相邻像素更可能属于同一对象（类），而不是不同对象。随着传感器分辨率的不断提高，光滑度假设变得越来越重要，这既是因为类别内的辐射度可变性增加，又是因为在阴影和阴影效应，多种材料等更异构的区域（例如城市）采用了遥感技术，降低测量数据，并且先验知识将发挥更大的作用。本文对图像分类方法进行了系统的概述，该方法在标签上施加了平滑度。综述了在图像处理其他领域开发的局部滤波类型方法和全局随机域模型，并提出了两种新方法。然后，使用来自市区的已知地面事实的两个不同的航空数据集，对提出的方法进行详细的实验比较和分析。该论文的主要信息是，在对高空间分辨率的数据进行分类时，平滑度可大大提高结果的准确性-在我们的实验中高达33％。进一步的发现是，全局随机场模型的性能优于局部滤波方法，应该更广泛地用于遥感。最后，评估结果确认所有方法在最有效时都已经过时了，并指出有必要在分类过程中包括越来越多的先验信息。

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