Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of s...

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Bibliographic Details
Published in:Discrete dynamics in nature and society 2021, Vol.2021, p.1-11
Main Authors: Chen, Eryang, Chang, Ruichun, Shi, Kaibo, Ye, Ansheng, Miao, Fang, Yuan, Jianghong, Guo, Ke, Wei, Youhua, Li, Yiping
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Datasets
Deep learning
Discriminant analysis
Feature extraction
Graph theory
Hyperspectral imaging
Image classification
Noise
Parameters
Pattern analysis
Performance evaluation
Principal components analysis
Sensors
Spatial data
Spectra
Teaching methods
Training
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Summary:Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.
ISSN:1026-0226
1607-887X
DOI:10.1155/2021/9998185