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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. [ABSTRACT FROM AUTHOR]

Sparse representation of signals for classification is an active research area. Signals can potentially have a compact representation as a linear combination of atoms in an overcomplete dictionary. Based on this observation, a sparserepresentation- based classification (SRC) has been proposed for robust face recognition and has gained popularity for various classification tasks. It relies on the underlying assumption that a test sample can be linearly represented by a small number of training samples from the same class. However, SRC implementations ignore the Euclidean distance relationship between samples when learning the sparse representation of a test sample in the given dictionary. To overcome this drawback, we propose an alternate formulation that we assert is better suited for classification tasks. Specifically, class-dependent sparse representation classifier (cdSRC) is proposed for hyperspectral image classification, which effectively combines the ideas of SRC and K-nearest neighbor classifier in a classwise manner to exploit both correlation and Euclidean distance relationship between test and training samples. Toward this goal, a unified class membership function is developed, which utilizes residual and Euclidean distance information simultaneously. Experimental results based on several real-world hyperspectral data sets have shown that cdSRC not only dramatically increases the classification performance over SRC but also outperforms other popular classifiers, such as support vector machine. [ABSTRACT FROM AUTHOR]