Incremental and Decremental Affinity Propagation for Semisupervised Clustering in Multispectral Images

Clustering is used for land-cover identification in remote sensing images when training data are not available. However, in many applications, it is often possible to collect a small number of labeled samples. To effectively exploit this small number of labeled samples combined with a multitude of the unlabeled data, we present a novel semisupervised clustering technique [incremental and decremental affinity propagation (ID-AP)] that incorporates labeled exemplars into the AP algorithm. Unlike standard semisupervised clustering methods, the proposed technique improves the performance by using both the labeled samples to adjust the similarity matrix and an ID-learning principle for unlabeled data selection and useless labeled samples rejection, respectively. This avoids both learning-bias and stability-plasticity dilemma. In order to assess the effectiveness of the proposed ID-AP technique, the experimental analysis was carried out on three different kinds of multispectral images including different percentages of labeled samples. In the analysis, we also studied the accuracy and the stability of two semisupervised clustering algorithms [i.e., constrained k-means and semisupervised AP (SAP)] and one incremental semisupervised clustering algorithm (i.e., incremental SAP). Experimental results demonstrate that the proposed ID-AP technique adequately captures and takes full advantage of the intrinsic relationship between the labeled samples and unlabeled data, and produces better performance than the other considered methods.