Abstract

Hyperspectral image (HSI) classification has attracted much attention in the field of remote sensing. However, the lack of sufficient labeled training samples is a huge challenge for HSI classification. To face this challenge, we propose a semisupervised HSI classification method based on graph convolutional broad network (GCBN). First, to avoid the underfitting problem caused by the insufficient linear sparse feature representation ability of broad learning system (BLS), graph convolution operation is applied to extract nonlinear and discriminative spectral-spatial features from the original HSI to replace the linear mapping features in the traditional BLS. Second, to solve the problem of insufficient model classification ability caused by limited labeled samples, the combinatorial average method (CAM) is proposed to use valuable paired samples to generate sample expansion set for GCBN model training. Third, BLS is used to perform broad expansion on spectral-spatial features extracted by GCN and extended by CAM, which further enhances the feature representation ability. Finally, the output weights can be easily calculated by the ridge regression theory. Experimental results on three real HSI datasets demonstrate the effectiveness of our proposed GCBN.

Highlights

  • H YPERSPECTRAL images (HSI) contain rich spectral and spatial information, which makes it widely used in crop monitoring, environmental monitoring, mineral exploration and other fields [1]-[4]

  • HSI classification is one of the basic and key technologies of remote sensing for earth surface observation. It aims to infer the class of each pixel based on the spectral and spatial information of the HSI [5][7].The early-staged methods for HSI classification are mostly based on conventional pattern recognition methods, such as Knearest neighbor [8] and Support vector machine (SVM) [9], random forest [10], and decision tree [11]

  • The main contributions of our work are summarized as follows: 1) We replace the linear mapping features used in the traditional broad learning system (BLS) with the spectral-spatial features extracted from the original HSI by GCN, which can achieve accurate HSI classification at low labeling cost by means of exploiting limited labeled samples and abundant unlabeled samples; 2) In the proposed combinatorial average method (CAM), some valuable paired samples are selected in a targeted manner, and averaged in pairs to generate a sample expansion set much larger than the original training set

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Summary

INTRODUCTION

H YPERSPECTRAL images (HSI) contain rich spectral and spatial information, which makes it widely used in crop monitoring, environmental monitoring, mineral exploration and other fields [1]-[4]. The main contributions of our work are summarized as follows: 1) We replace the linear mapping features used in the traditional BLS with the spectral-spatial features extracted from the original HSI by GCN, which can achieve accurate HSI classification at low labeling cost by means of exploiting limited labeled samples and abundant unlabeled samples; 2) In the proposed combinatorial average method (CAM), some valuable paired samples are selected in a targeted manner, and averaged in pairs to generate a sample expansion set much larger than the original training set. The problem of the lack of labeled samples to support high-precision classification model training can be solved; 3) We exploit the BLS to perform broad expansion on spectral-spatial features extracted by GCN and extended by CAM, which is helpful to further enhance the representation ability of features and improve the classification accuracy of HSI.

Flowchart of GCBN for HSI Classification
Feature extraction based on GCN
Sample expansion based on CAM
Spectral-spatial feature broad expansion based on BLS
HSI datasets
Experimental result
CONCLUSION
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