University of the Witwatersrand, South Africa.
IEEE Trans Pattern Anal Mach Intell. 2010 Nov;32(11):1977-93. doi: 10.1109/TPAMI.2010.37.
This paper presents a new method for unsupervised subpixel change detection using image series. The method is based on the definition of a probabilistic criterion capable of assessing the level of coherence of an image series relative to a reference classification with a finer resolution. In opposition to approaches based on an a priori model of the data, the model developed here is based on the rejection of a nonstructured model-called a-contrario model-by the observation of structured data. This coherence measure is the core of a stochastic algorithm which automatically selects the image subdomain representing the most likely changes. A theoretical analysis of this model is led to predict its performances, in particular regarding the contrast level of the image as well as the number of change pixels in the image. Numerical simulations are also presented that confirm the high robustness of the method and its capacity to detect changes impacting more than 25 percent of a considered pixel under average conditions. An application to land-cover change detection is then provided using time series of satellite images.
本文提出了一种利用图像序列进行无监督亚像素变化检测的新方法。该方法基于定义一个概率准则，该准则能够评估图像序列相对于具有更精细分辨率的参考分类的相干水平。与基于数据先验模型的方法相反，这里开发的模型基于通过观察结构化数据来拒绝非结构化模型（称为相反模型）。该相干度量是一种随机算法的核心，该算法可自动选择最有可能发生变化的图像子域。对该模型进行了理论分析，以预测其性能，特别是关于图像的对比度水平以及图像中变化像素的数量。还进行了数值模拟，以确认该方法的高度稳健性及其在平均条件下检测影响所考虑像素 25%以上的变化的能力。然后，使用卫星图像的时间序列提供了土地覆盖变化检测的应用。
