保持局域特征的多源海冰图像融合与分类

本文提出一种保持局域特征的多源海冰图像融合方法，并在此基础上进行海冰分类。本文提出的多源海冰图像融合方法包括保持空间局域融合和保持特征局域融合两方面。首先，通过学习得到投影矩阵和相似矩阵。投影矩阵将多源像素进行投影变换，得到保留像素空间局域特性的融合向量。相似矩阵度量像素特征间的相似性，通过拉普拉斯特征分解，得到保留像素特征局域相似性的融合向量。然后，将空间融合向量和特征融合向量进行像素综合，得到融合图像。在此基础上，本文设计一种滑动集成分类方法进行融合图像像素分类。提出的分类方法利用滑动集成的特点，在分类时增强刻画了海冰局域特性。由于本文的保持局域融合框架不仅刻画了海冰在物理空间中的邻接关系，而且考虑不同海冰类型的特征关系，因此其在多源图像（多光谱和合成孔径雷达（SAR）图像）的海冰分类任务中表现优异。实验结果表明本文提出的基于保持局域特征融合的多源海冰图像分类方法有效提升了海冰分类精度。

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.