Joint Euclidean and Angular Distance-Based Embeddings for Multisource Image Analysis

With the emergence of passive and active optical sensors available for geospatial imaging, information fusion across sensors is becoming ever more important. An important aspect of single (or multiple) sensor geospatial image analysis is feature extraction-the process of finding “optimal” lower dimensional subspaces that adequately characterize class-specific information for subsequent analysis tasks, such as classification, change and anomaly detection, and so on. In recent work, we proposed and developed an angle-based discriminant analysis approach that projected data onto the subspaces with maximal “angular” separability in the input (raw) feature space and reproducing kernel Hilbert space. We also developed an angular locality preserving variant of this algorithm. Despite being a promising approach, the resulting subspace does not preserve Euclidean distance information. In this letter, we advance this work to address that limitation and make it suitable for information fusion-we propose and validate a composite kernel-based subspace learning framework that simultaneously preserves Euclidean and angular information, which can operate on an ensemble of feature sources (e.g., from different sources). We validate this method with the multisensor University of Houston hyperspectral and light detection and ranging data set, and demonstrate that a joint discriminant analysis that leverages angular and Euclidean distance information provides superior classification and sensor (information) fusion performance.