The availability of remote sensing images with high spectral, spatial and temporal resolutions has motivated the design of new change detection (CD) methods for surveying changes in a studied area. The challenge of unsupervised CD is to develop flexible automatic models to estimate changes. In this paper, we propose a novel hierarchical Bayesian model for CD. Our main contribution lies in the application of Bernoulli-based models to change detection and transforming it to a denoising problem. The originality is related to the capacity of these models to act as implicit classifiers in addition to the denoising effect since even for changed pixels noise is also removed. The second originality lies in the way inference is conducted. Specifically, the hierarchical Bayesian model and Gibbs sampler ensure building an algorithm with secure convergence guarantees. Experiments performed on real data indicate the benefit that can be drawn from our approach.

中文翻译：

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遥感图像的无监督贝叶斯变化检测

具有高光谱、空间和时间分辨率的遥感图像的可用性促使设计新的变化检测 (CD) 方法来调查研究区域的变化。无监督 CD 的挑战是开发灵活的自动模型来估计变化。在本文中，我们提出了一种新颖的 CD 分层贝叶斯模型。我们的主要贡献在于将基于伯努利的模型应用于变化检测并将其转化为去噪问题。原创性与这些模型作为隐式分类器的能力有关，除了去噪效果之外，因为即使是改变像素的噪声也被去除了。第二个独创性在于进行推理的方式。具体来说，分层贝叶斯模型和 Gibbs 采样器确保构建具有安全收敛保证的算法。对真实数据进行的实验表明可以从我们的方法中获得好处。