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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Vydané v:Wireless communications and mobile computing Ročník 2022; číslo 1
Hlavní autori: Liu, Baisen, Kong, Weili, Wang, Yan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Hindawi 2022
John Wiley & Sons, Inc
Predmet:
Algorithms
Asymmetry
Clustering
Coders
Discriminant analysis
Feature extraction
Hyperspectral imaging
Methods
Remote sensing
Representations
Spatial data
ISSN:1530-8669, 1530-8677
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/2027981