Immersive Interactive SAR Image Representation Using Non-negative Matrix Factorization

Abstract

Earth observation (EO) image clustering is a challenging problem in data mining, where each image is represented by a high-dimensional feature vector. However, the feature vectors might not be appropriate to express the semantic content of images, which eventually lead to poor results in clustering and classification. To tackle this problem, we propose an interactive approach to generate compact and informative features from images content. To this end, we utilize a 3-D interactive application to support user-image interactions. These interactions are used in the context of two novel nonnegative matrix factorization (NMF) algorithms to generate new features. We assess the quality of new features by applying k-means clustering to the generated features and compare the obtained clustering results with those achieved by original features. We perform experiments on a synthetic aperture radar (SAR) image dataset represented by different state-of-the-art features and demonstrate the effectiveness of the proposed method. Moreover, we propose a divide-and-conquer approach to cluster a massive amount of images using a small subset of interactions.