Feature-based non-parametric estimation of Kullback–Leibler divergence for SAR image change detection

Abstract

In this article, a method based on a non-parametric estimation of the Kullback-Leibler divergence using a local feature space is proposed for synthetic aperture radar (SAR) image change detection. First, local features based on a set of Gabor filters are extracted from both preand post-event images. The distribution of these local features from a local neighbourhood is considered as a statistical representation of the local image information. The Kullback-Leibler divergence as a probabilistic distance is used for measuring the similarity of the two distributions. Nevertheless, it is not trivial to estimate the distribution of a high-dimensional random vector, let alone the comparison of two distributions. Thus, a non-parametric method based on k-nearest neighbour search is proposed to compute the Kullback-Leibler divergence between the two distributions. Through experiments, this method is compared with other state-of-the-art methods and the effectiveness of the proposed method for SAR image change detection is demonstrated.

3D Buildings Change Detection from Aerial and Satellite Stereo Imagery Using Kullback–Leibler Divergence Algorithm

“…In case if the two probability density functions are close to each other, the Kullback-Leibler divergence is small. In contrast, it is larger if there is a great deviation between them [19]. The KLD is always nonnegative and equals zero only if the two distributions are identical.…”

Statistical Estimation of the Kullback–Leibler Divergence

“…The Kullback-Leibler divergence introduced in [1] is used for quantification of similarity of two probability measures. It plays important role in various domains such as statistical inference (see, e.g., [2,3]), metric learning [4,5], machine learning [6,7], computer vision [8,9], network security [10], feature selection and classification [11][12][13], physics [14], biology [15], medicine [16,17], finance [18], among others. It is worth to emphasize that mutual information, widely used in many research directions (see, e.g., [19][20][21][22][23]), is a special case of the Kullback-Leibler divergence for certain measures.…”

Statistical estimation of the Kullback-Leibler divergence

“…The Kullback -Leibler divergence plays important role in various domains such as statistical inference (see, e.g., [25], [28]), machine learning ( [5], [32]), computer vision ( [11], [13]), network security ( [23], [44]), feature selection and classification ( [22], [29], [41]), physics ( [17]), biology ( [9]), finance ( [45]), among others. Recall that this divergence measure between probabilities P and Q on a space (S, B) is defined by way of…”