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

Addressing the challenge of multiclass change detection with a small sample size, a change vector analysis with an adaptive weight Gaussian mixture model (CVA-AWGMM) is proposed in this article. Initially, to avoid errors caused by geometric distortion and imprecise alignment, we employed a neighborhood search approach when calculating the different maps. 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 (BCD) result, a semisupervised Gaussian mixture model (ssGMM) is used for further change class differentiation. Recognizing the inherent challenges of effectively training a 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 weigh the features of both to further improve the accuracy of the method. Experiments were conducted on two public datasets and one self-made dataset, and the results demonstrate that the proposed CVA-AWGMM outperforms several typical methods.