Sparse tensor neighbor embedding based pan-sharpening via N-way block pursuit
Sparse tensor neighbor embedding based pan-sharpening via N-way block pursuit
复制标题
基于稀疏张量邻域嵌入的 N 路块追踪全色锐化
DOI：
10.1016/j.knosys.2018.01.022
复制
发表时间：
2018
影响因子：
8.8
通讯作者：
Yang Shuyuan
中科院分区：
文献类型：
--
作者：
Wang Min;Zhang Kai;Pan Xi;Yang Shuyuan
现有的全色锐化方法大多采用基于矢量或矩阵的细节注入来提高多光谱图像的分辨率，但这会导致图像的光谱和空间失真。在本文中，我们探讨的内在张量结构和局部稀疏的MS图像，开发一种新的稀疏张量相邻嵌入（STNE）的泛锐化方法，减少在融合图像的失真。首先，MS图像被公式化为一些光谱张量，并假设每个张量及其最近邻张量位于一个低维流形。然后将张量稀疏编码到相邻张量之下，并在频带上引入联合稀疏编码假设，从而发展了求解稀疏张量系数的N路块追踪算法.最后利用稀疏张量系数对全色图像进行加权，得到高分辨率的MS张量。张量是向量和矩阵的高阶推广，利用多维数据的高阶结构可以帮助我们理解它们。该方法首先将稀疏张量与邻域嵌入相结合，构造了一种新的高维稀疏张量嵌入，实现了有效的泛锐化。由于张量公式可以利用高维MS数据中的结构相关性，因此该方法可以同时很好地保留不同波段之间的光谱相关性，并捕获MS图像的高阶统计特性。真实的QuickBird和GeoEye数据集上进行了实验，实验结果表明STNE在降低光谱和空间失真方面上级同类算法。
Most of the available pan-sharpening methods use vector or matrix based detail injection to enhance the resolution of MultiSpectral (MS) image, which may result in unavoidable spectral and spatial distortions. In this paper we explore the intrinsic tensor structure and local sparsity of MS images, to develop a novel Sparse Tensor Neighbor Embedding (STNE) based pan-sharpening method that reduces the distortions in the fused images. First, MS images are formulated as some spectral tensors, and each tensor and its nearest neighbor tensors are assumed to lie in a low-dimensional manifold. Then the tensor is sparsely coded under its neighbor tensors, and a joint sparse coding assumption is cast on bands to develop an N-way Block Pursuit algorithm for solving sparse tensor coefficients. Finally high resolution MS tensor can be obtained by weighting Panchromatic image with the sparse tensor coefficients. Tensors are higher order generalizations of vectors and matrices, and taking advantage of high-order structure of multi-dimensional data can help us understand them. The proposed method first combines a sparse tensor with neighbor embedding, to construct a new high-dimensional sparse tensor embedding for efficient pan-sharpening. Because tensor formulation can exploit the structural correlations in high-dimensional MS data, the proposed method can well preserve spectral correlation among different bands simultaneously and capture the underlying high-order statistical properties of MS image. Some experiments are performed on several real QuickBird and GeoEye datasets, and experimental results show that STNE is superior to its counterparts in reducing spectral and spatial distortions.