Locality preserving fusion of multi-source images for sea-ice classification

We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information. The locality preserving fusion arises from two-fold, i.e., the local characterization in both spatial and feature domains. We commence by simultaneously learning a projection matrix, which preserves spatial localities, and a similarity matrix, which encodes feature similarities. We map the pixels of multi-source images by the projection matrix to a set fusion vectors that preserve spatial localities of the image. On the other hand, by applying the Laplacian eigen-decomposition to the similarity matrix, we obtain another set of fusion vectors that preserve the feature local similarities. We concatenate the fusion vectors for both spatial and feature locality preservation and obtain the fusion image. Finally, we classify the fusion image pixels by a novel sliding ensemble strategy, which enhances the locality preservation in classification. Our locality preserving fusion framework is effective in classifying multi-source sea-ice images (e.g., multi-spectral and synthetic aperture radar (SAR) images) because it not only comprehensively captures the spatial neighboring relationships but also intrinsically characterizes the feature associations between different types of sea-ices. Experimental evaluations validate the effectiveness of our framework.