Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

Abstract

Abstract:The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial-spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method.

Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel $k$-means, and Heat Kernel

“…Comparing to the production of hypergraph cut objectives as above, ways of modeling as hypergraphs have received less attention. There are various studies to model real-valued data as hypergraphs by heuristic ways (Govindu 2005;Sun et al 2017;Yu et al 2018). However, to our knowledge, no studies developed a hypergraph cut-based framework to model real-valued data as hypergraphs.…”

“…By looking at multi-way relationships, we aim to gain better clustering results for general data as well as to model data that essentially involves multi-way relationships, such as the examples above. However, while heuristic modeling as hypergraphs has been done in several domains (Govindu 2005;Sun et al 2017;Yu et al 2018), we are yet to have a modeling framework that is theoretically connected to hypergraph cut problems.…”

Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel k-Means, and Heat Kernel

“…By looking at multi-way relationships, we aim to gain better clustering results for general data as well as to model data that essentially involves multi-way relationships, such as the examples above. However, while heuristic modeling as hypergraphs has been done in several domains (Govindu 2005;Sun et al 2017;Yu et al 2018), we are yet to have a modeling framework that is theoretically connected to hypergraph cut problems.…”

Variational Generative Adversarial Network with Crossed Spatial and Spectral Interactions for Hyperspectral Image Classification

“…Hyperspectral images (HSI) contain hundreds of continuous and diverse bands rich in spectral and spatial information, which can distinguish land-cover types more efficiently compared with ordinary remote sensing images [1,2]. In recent years, Hyperspectral images classification (HSIC) has become one of the most important tasks in the field of remote sensing with wide application in scenarios such as urban planning, geological exploration, and agricultural monitoring [3][4][5][6].…”