Hyperspectral Image Classification Based on Stacked Contractive Autoencoder Combined With Adaptive Spectral-Spatial Information

Hyperspectral image (HSI) contain abundant spectral and spatial information, enabling the accurate classification of ground objects. However, many existing machine learning methods have poor performance, and some existing CNN-based methods require high computational power, which considerably limits...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 96404 - 96415
Main Authors: Guo, Pengyue, Liu, Zhenbing, Lu, Haoxiang, Wang, Zimin
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
adaptive spectral-spatial information
Adaptive systems
Classification
Data mining
Datasets
Feature extraction
Hyperspectral image
Hyperspectral imaging
Image classification
Image reconstruction
Information retrieval
logistic regression
Logistics
Machine learning
Spatial data
Spectra
Transforms
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Hyperspectral image (HSI) contain abundant spectral and spatial information, enabling the accurate classification of ground objects. However, many existing machine learning methods have poor performance, and some existing CNN-based methods require high computational power, which considerably limits their real-world applications. To address these issues, in this paper, we propose an alternative HSI classification method based on the stacked contractive autoencoder (SCAE) and adaptive spectral-spatial information to improve the accuracy of HSI classification. Specifically, the non-subsampled shearlet transform (NSST) with the guided filtering (NG) enhances spatial structure information. Subsequently, we present an adaptive spatial information extraction method to extract the spatial information of pixels. Furthermore, we propose an HSI classification network, called SCAE-LR, for feature extraction and classification. The SCAE is implemented to extract the adaptive spectral-spatial feature, and a logistic regression (LR) layer is employed for classification. Extensive experiments on the Indian Pines data set and the Pavia University data set demonstrate the superior performance of our method.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3095265