Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs

Feng Gao,Junyu Dong,Qizhi Xu,Qun Wang

doi:10.3390/rs10081271

Feng Gao, Junyu Dong + Show 2 more

Open Access

https://doi.org/10.3390/rs10081271

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Journal: Remote Sensing	Publication Date: Aug 12, 2018
Citations: 52	License type: CC BY 4.0

Affiliation: Ocean University of China, Beihang University

Abstract

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

Highlights

With the advance of earth observation programs, many hyperspectral sensors with high spectral resolution have been developed, such as NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and NASA’s EO-1 with its hyperspectral instrument Hyperion
The experimental results on this dataset demonstrate that the Random Multi-Graphs algorithm is effective in hyperspectral image classification, especially when the number of training samples is limited
It is evident that the proposed SS-Random Multi-Graphs (RMGs) outperforms local binary pattern (LBP)-ELM by randomly selecting subsets of features

Summary

Introduction

With the advance of earth observation programs, many hyperspectral sensors with high spectral resolution have been developed, such as NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and NASA’s EO-1 with its hyperspectral instrument Hyperion. Hyperspectral images have drawn increasing attention and opened up new remote sensing application fields, such as hydrocarbon detection [1], lake sediment analysis [2], oil reservoir exploration [3], and diseased wheat detection [4], etc. Among these applications, classification of hyperspectral images is well acknowledged as the fundamental and challenging task of hyperspectral data processing. Hyperspectral image classification has been widely studied in the last two decades [5]

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Conclusion