SLCRF: Subspace Learning With Conditional Random Field for Hyperspectral Image Classification

Subspace learning (SL) plays an essential role in hyperspectral image (HSI) classification since it can provide an effective solution to reduce the redundant information in the image pixels of HSIs. Previous works about SL aim to improve the accuracy of HSI recognition. Using a large number of label...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 59; no. 5; pp. 4203 - 4217
Main Authors: Cao, Yun, Mei, Jie, Wang, Yuebin, Zhang, Liqiang, Peng, Junhuan, Zhang, Bing, Li, Lihua, Zheng, Yibo
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Classification
Conditional random field (CRF)
Conditional random fields
Data models
Decoding
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Iterative algorithms
Iterative methods
Labour
Learning
Linear programming
Objective function
optimization
Pixels
Probability theory
relationship construction
Spatial data
subspace learning (SL)
Training
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Subspace learning (SL) plays an essential role in hyperspectral image (HSI) classification since it can provide an effective solution to reduce the redundant information in the image pixels of HSIs. Previous works about SL aim to improve the accuracy of HSI recognition. Using a large number of labeled samples, related methods can train the parameters of the proposed solutions to obtain better representations of HSI pixels. However, the data instances may not be sufficient to learn a precise model for HSI classification in real applications. Moreover, it is well known that it takes much time, labor, and human expertise to label HSI images. To avoid the abovementioned problems, a novel SL method that includes the probability assumption called SL with the conditional random field (SLCRF) is developed. In SLCRF, the 3-D convolutional autoencoder (3DCAE) is first introduced to remove the redundant information in HSI pixels. Besides, the relationships are also constructed using spectral-spatial information among the adjacent pixels. Then, the conditional random field (CRF) framework can be constructed and further embedded into the HSI SL procedure with the semisupervised approach. Through the linearized alternating direction method termed LADMAP, the objective function of SLCRF is optimized using a defined iterative algorithm. The proposed method is comprehensively evaluated using the challenging public HSI data sets. We can achieve state-of-the-art performance using these HSI sets.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3011429