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Conventional unsupervised classification algorithms that model the data in each class with a multivariate Gaussian distribution are often inappropriate, as this assumption is frequently not satisfied by the remote sensing data. In this Letter, a new algorithm based on independent component analysis (ICA) is presented. The ICA mixture model (ICAMM) algorithm that models class distributions as non-Gaussian densities has been employed for unsupervised classification of a test image from the AVIRIS sensor. A number of feature-extraction techniques have also been examined that serve as a preprocessing step to reduce the dimensionality of the hyperspectral data. The proposed ICAMM algorithm results in significant increase in the classification accuracy over that obtained from the conventional K-means algorithm for land cover classification.

Alignment of many 3D point clouds, possibly captured by multiple devices at different times, is a critical step for increasingly popular applications such as 3D model construction and augmented reality. For very large data sets, traditional methods such as ICP can become computationally intractable, or produce poor results. We present an efficient method for accurately aligning very large numbers of dense 3D point clouds, and apply it to a city-scale data set. The method relies on the novel combination of 1) partitioning the point clouds based on loop structures detected across a combined network of all device capture paths, and 2) making use of the loop closure property to accurately align point clouds within each sub-problem. Final global alignment of the loop-based results is formulated as a least squares optimization with closed form solution. Experimental results are shown for aligning 3D points across the entire city of San Francisco with centimeter-scale accuracy, via an efficient parallelized architecture.

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