A Small Sample-Based Multiclass Change Detection Method Using Change Vector Analysis With Adaptive Weight Gaussian Mixture Model

Abstract

Addressing the challenge of multi-class change detection with a small sample size, a change vector analysis with adaptive weight Gaussian mixture model (CVA-AWGMM) is proposed in this article. Initially, in order to avoid errors caused by geometric distortion and imprecise alignment, we employed a neighborhood search approach when calculating the different map. Instead of direct pixel-to-pixel comparisons, we compensated corresponding pixels in the images before and after the change by finding the minimum difference within a specified pixel range. Following this, we employ the fundamental framework of CVA to extract magnitude and angle features from the change vectors, facilitating the identification of both changed and unchanged regions through threshold segmentation. Based on the above binary change detection result, a semi-supervised Gaussian mixture model is used for further change class differentiation. Recognizing the inherent challenges of effectively training classifier model with a small sample size, the clustering features in a large number of unlabeled samples and the supervised information from a few labeled samples are simultaneously utilized to co-construct the objective function of the model. Meanwhile, considering the complementary properties of magnitude and angle features, the labeled samples are used to adaptively weight the features of both to further improve the accuracy of the method. Experiments were conducted on two public data sets and one self-made data set, and the results demonstrate that the proposed CVA-AWGMM outperforms several typical methods.

Topics & Concepts