Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch Alignment

Abstract

To take full advantage of hyperspectral information, to avoid data redundancy and to address the curse of dimensionality concern, dimensionality reduction (DR) becomes particularly important to analyze hyperspectral data. Exploring the tensor characteristic of hyperspectral data, a DR algorithm based on class-aware tensor neighborhood graph and patch alignment is proposed here. First, hyperspectral data are represented in the tensor form through a window field to keep the spatial information of each pixel. Second, using a tensor distance criterion, a class-aware tensor neighborhood graph containing discriminating information is obtained. In the third step, employing the patch alignment framework extended to the tensor space, we can obtain global optimal spectral-spatial information. Finally, the solution of the tensor subspace is calculated using an iterative method and low-dimensional projection matrixes for hyperspectral data are obtained accordingly. The proposed method effectively explores the spectral and spatial information in hyperspectral data simultaneously. Experimental results on 3 real hyperspectral datasets show that, compared with some popular vector- and tensor-based DR algorithms, the proposed method can yield better performance with less tensor training samples required.

The Tensor‐based Feature Analysis of Spatiotemporal Field Data With Heterogeneity

“…In the tensor-based geoscience application, the tensor structure is first utilized to integrate the spatial and temporal information in a unified framework. Then, with the capability of the tensor decomposition for feature extraction, the nonlinear signal extraction (Lu et al, 2018), feature-based compressed storage (Yuan et al, 2015), and dimensionality reduction (Gao et al, 2015), can be achieved by absorbing the prominent spatiotemporal features and removing the redundancy. All these tensor-based spatiotemporal analyses show the state-of-the-art performance for spatiotemporal data (Leibovici, 2010b).…”

A novel locality-sensitive hashing algorithm for similarity searches on large-scale hyperspectral data

“…Similarity search is a fundamental pre-processing method for many applications of hyperspectral remote sensing data sets (HRD), such as manifold learning-based dimensionality reduction (Gao et al 2015), graph-based classification (Bai, Xiang, and Pan 2013), spectral un-mixing (Ammanouil, Ferrari, and Richard 2015), etc. Exhaustive similarity search is a general problem-solving technique, but it will soon become the computation-bottleneck of the above applications for processing the large-scale data set.…”