Semisupervised Manifold Joint Hypergraphs for Dimensionality Reduction of Hyperspectral Image

Duan, Yule; Hong, Huixiao; Tang, Yuxiao; Li, Yuan; Pu, Chunyu

doi:10.1109/lgrs.2020.3009144

Cited by 7 publications

(1 citation statement)

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“…The modification performed in the loss function greatly reduces the scatters and improves the classification performances compared to existing methodologies. The dimensionality reduction model for a Hyperspectral image incorporates a graph-based approach for effective dimensional reduction [23]. The presented multigraph embedding procedure initially captures the local and spatial information to develop a tensor subgraph.…”

Section: Related Workmentioning

confidence: 99%

Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

Srinivasan¹,

Rajakumar²

2023

Computer Systems Science and Engineering

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The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors. The high correlation between these features and the noises greatly affects the classification performances. To overcome this, dimensionality reduction techniques are widely used. Traditional image processing applications recently propose numerous deep learning models. However, in hyperspectral image classification, the features of deep learning models are less explored. Thus, for efficient hyperspectral image classification, a depth-wise convolutional neural network is presented in this research work. To handle the dimensionality issue in the classification process, an optimized self-organized map model is employed using a water strider optimization algorithm. The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances. Standard datasets such as Indian Pines and the University of Pavia (UP) are considered for experimental analysis. Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning (EHGDL), local geometric structure Fisher analysis (LGSFA), Discriminant Hyper-Laplacian projection (DHLP), Group-based tensor model (GBTM), and Lower rank tensor approximation (LRTA) methods are compared with proposed optimized SOM model. Results confirm the superior performance of the proposed model of 98.22% accuracy for the Indian pines dataset and 98.21% accuracy for the University of Pavia dataset over the existing maximum likelihood classifier, and Support vector machine (SVM).

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Section: Related Workmentioning

confidence: 99%