Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotations have been collected in the vision community. In this paper, we attempt to exploit the abundant labeled ground-level images to build discriminative models for overhead-view RSI classification. However, images from the ground-level and overhead view are represented by heterogeneous features with different distributions; how to effectively combine multiple features and reduce the mismatch of distributions are two key problems in this scene-model transfer task. Specifically, a semi-supervised manifold-regularized multiple-kernel-learning (SMRMKL) algorithm is proposed for solving these problems. We employ multiple kernels over several features to learn an optimal combined model automatically. Multi-kernel Maximum Mean Discrepancy (MK-MMD) is utilized to measure the data mismatch. To make use of unlabeled target samples, a manifold regularized semi-supervised learning process is incorporated into our framework. Extensive experimental results on both cross-view and aerial-to-satellite scene datasets demonstrate that: (1) SMRMKL has an appealing extension ability to effectively fuse different types of visual features; and (2) manifold regularization can improve the adaptation performance by utilizing unlabeled target samples.

We propose a novel framework for large-scale maritime ship group surveillance using spaceborne optical imaging satellite data and Electronic Intelligence (ELINT) satellite data. Considering that the size of a ship is usually less than the distance between different ships for large-scale maritime surveillance, we treat each ship as a mass point and ship groups are modelled as point sets. Motivated by the observation that ship groups performing tactical or strategic operations often have a stable topology and their attributes remain unchanged, we combine both topological features and attributive features within the framework of Dempster-Shafer (D-S) theory for coherent ship group analysis. Our method has been tested using different sets of simulated data and recorded data. Experimental results demonstrate our method is robust and efficient for large-scale maritime surveillance.