Bruzzone Lorenzo, Prieto Diego Fernàndez
Dept. of Inf. and Commun. Technol., Trento Univ., Italy.
IEEE Trans Image Process. 2002;11(4):452-66. doi: 10.1109/TIP.2002.999678.
In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semiparametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov random field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semiparametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach.
本文提出了一种新颖的自动方法，用于对多时相遥感图像中的变化进行无监督识别。与传统方法不同，该方法基于贝叶斯决策理论来阐述无监督变化检测问题。在此背景下，提出了一种自适应半参数技术，用于无监督估计差异图像中与变化像素和未变化像素灰度级相关的统计项。这种技术利用了两种理论基础扎实的估计程序的有效性：简化的帕曾估计（RPE）程序和期望最大化（EM）算法。然后，借助所得估计值以及用于对所考虑的多时相图像中包含的空间上下文信息进行建模的马尔可夫随机场（MRF）方法，生成变化检测图。所提技术的自适应半参数性质使其能够应用于不同类型的遥感图像。在由两种不同传感器获取的两组多时相遥感图像上获得的实验结果证实了所提方法的有效性。
