Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

Zhaohui Xue,Hongyan Zhang,Peijun Du,Sirui Yang

doi:10.3390/rs11171959

Abstract

Hyperspectral and light detection and ranging (LiDAR) data fusion and classification has been an active research topic, and intensive studies have been made based on mathematical morphology. However, matrix-based concatenation of morphological features may not be so distinctive, compact, and optimal for classification. In this work, we propose a novel Coupled Higher-Order Tensor Factorization (CHOTF) model for hyperspectral and LiDAR data classification. The innovative contributions of our work are that we model different features as multiple third-order tensors, and we formulate a CHOTF model to jointly factorize those tensors. Firstly, third-order tensors are built based on spectral-spatial features extracted via attribute profiles (APs). Secondly, the CHOTF model is defined to jointly factorize the multiple higher-order tensors. Then, the latent features are generated by mode-n tensor-matrix product based on the shared and unshared factors. Lastly, classification is conducted by using sparse multinomial logistic regression (SMLR). Experimental results, conducted with two popular hyperspectral and LiDAR data sets collected over the University of Houston and the city of Trento, respectively, indicate that the proposed framework outperforms the other methods, i.e., different dimensionality-reduction-based methods, independent third-order tensor factorization based methods, and some recently proposed hyperspectral and LiDAR data fusion and classification methods.

Highlights

Remote sensing technologies are vital for Earth observation since they can provide a variety information about the structure, elevation, and material content of the Earth’s surface objects [1]
We propose a novel coupled high-order tensor factorization model for hyperspectral and light detection and ranging (LiDAR) data fusion and classification, which is unique compared with regard to previously proposed approaches in this area
When using Principal Component Analysis (PCA) to build extended multi-attribute profile (EMAP)(XH), the features extracted by PCA preserving more than 99.9% information according to the cumulative variance, i.e., 6 principal components (PCs) for University of Houston data sets, and 8 PCs for Trento data sets

Summary

Introduction

Remote sensing technologies are vital for Earth observation since they can provide a variety information about the structure (optical or radar), elevation (light detection and ranging, LiDAR), and material content (multispectral or hyperspectral) of the Earth’s surface objects [1]. In the scenario of differentiating objects with the same material or elevation, single technology is usually insufficient for producing reliable results. In this context, hyperspectral and LiDAR data fusion has been exploited to address this issue, which is a hot topic and has been attracted great attention by geoscience and remote sensing society in recent years [3]

Methods

Results

Discussion

Conclusion