Spectral active clustering of remote sensing images

Mining useful information from remote sensing images is a longstanding and challenging problem in earth observation, among which images clustering is used to discover meaningful scene information, by grouping similar image pixels into clusters. The main difficulty of image clustering, however, lies in the fact that imperfect similarity measure between images usually leads to bad clustering results. Supervised classification with labeled training samples can partially solve this problem, but the collection of such labeled data is usually time-consuming and sometimes impossible in many real problems. This paper presents an active remote sensing image clustering algorithm by integrating simple human queries into the clustering process. More precisely, we propose a spectral active clustering method that can actively query the oracle (such as human) to improve the image clustering performance. We first construct a k-nearest neighbor (k-NN) graph of the remote sensing images. We then iteratively select the most informative pairwise constraints and purify the k-NN graph, by removing the edges between images from different classes. The final clustering on the purified k-NN graph leads to more accurate result. The proposed method has been evaluated on three high-resolution remote sensing image datasets. It achieves the state-of-the-art performance and demonstrates high potentials in practical remote sensing applications.