Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image

Due to the complex properties of hyperspectral images (HSI), such as spatial-spectral structure, high dimension, and great spectral variability, HSI clustering is a challenging operation. In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering ne...

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Bibliographic Details
Published in:Wireless communications and mobile computing Vol. 2022; no. 1
Main Authors: Liu, Baisen, Kong, Weili, Wang, Yan
Format: Journal Article
Language:English
Published: Oxford Hindawi 2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Asymmetry
Clustering
Coders
Discriminant analysis
Feature extraction
Hyperspectral imaging
Methods
Remote sensing
Representations
Spatial data
ISSN:1530-8669, 1530-8677
Online Access:Get full text
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Summary:Due to the complex properties of hyperspectral images (HSI), such as spatial-spectral structure, high dimension, and great spectral variability, HSI clustering is a challenging operation. In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net) which employs a convolutional autoencoder (CAE) and an asymmetric autoencoder to investigate spatial-spectral information. First, we use a CAE to extract spatial-spectral features. Then, we introduce an asymmetric autoencoder between the encoder and decoder of CAE to suppress some non-material-related spatial information in latten feature like shading and texture. By using a collaborative strategy to train the proposed networks, we obtain the representation features in a low dimension. Furthermore, we improve the k-means algorithm by using the concept of over-clustering to handle fuzzy representation which is difficult to distinguish the cluster, and utilize it to obtain the final HSI clustering result. The results of the experiments demonstrated that the proposed methodology outperforms other methods on the frequently used hyperspectral image dataset.
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ISSN:1530-8669
1530-8677
DOI:10.1155/2022/2027981