Self-Paced Probabilistic Collaborative Representation for Anomaly Detection of Hyperspectral Images

In recent years, hyperspectral anomaly detection methods based on representation models have attracted much attention. However, when the dictionary is polluted by anomalous pixels, their performance is greatly affected. To adjust the contributions of different dictionary atoms, traditional methods usually predefine a distance weighting matrix and impose it on the dictionary matrix or coefficient vector, which may not be accurate enough. To solve this problem, a self-paced probabilistic collaborative representation detector (SP-ProCRD) is proposed in this article. It assigns weights for each atom loss term according to the probability that the pixel under test (PUT) belongs to the same class as each dictionary atom. Unlike the predefined weight matrix approach, a self-paced learning (SPL) strategy is used for iterative optimization, so that dictionary atoms participate in the representation from "good" to "bad" ones when solving the model. The representation residuals are utilized to accelerate the convergence. The proposed model can optimally represent each PUT using similar dictionary atoms and minimize the negative impact caused by anomalous atoms contained in the dictionary. In terms of weighting for SPL, an adaptive weighting scheme based on the polynomial self-paced (SP) regularizer is proposed to address the generalization issues of most previous weighting schemes. This scheme improves the generalization and automation of the model. Experimental results reveal that the proposed method produces more accurate results than existing methods and runs efficiently.