A Pioneering Approach of Hyperspectral Image Classification Employing the Cooperative Efforts of 3D, 2D and Depthwise Separable-1D Convolutions

Ladi, Sandeep Kumar; Panda, Geetanjali; Dash, Ratnakar; Ladi, Pradeep Kumar

doi:10.1109/isssc56467.2022.10051566

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Bidari et al also employed a deep learning algorithm to classify hyperspectral images [34]. In addition, more recently, many researchers have proposed deep learning, including some dimension reduction to improve the efficiency of the hyperspectral-image-classification model [31,[35][36][37][38][39][40]. Liu et al applied a deep learning approach to classify and reconstruct hyperspectral images using MDL40w and achieved a 94.00% performance [41].…”

Section: Related Workmentioning

confidence: 99%

Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network

Hossain

Miah

et al. 2023

Electronics

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The ample amount of information from hyperspectral image (HSI) bands allows the non-destructive detection and recognition of earth objects. However, dimensionality reduction (DR) of hyperspectral images (HSI) is required before classification as the classifier may suffer from the curse of dimensionality. Therefore, dimensionality reduction plays a significant role in HSI data analysis (e.g., effective processing and seamless interpretation). In this article, a sophisticated technique established as t-Distributed Stochastic Neighbor Embedding (tSNE) following the dimension reduction along with a blended CNN was implemented to improve the visualization and characterization of HSI. In the procedure, first, we employed principal component analysis (PCA) to reduce the HSI dimensions and remove non-linear consistency features between the wavelengths to project them to a smaller scale. Then we proposed tSNE to preserve the local and global pixel relationships and check the HSI information visually and experimentally. Lastly, it yielded two-dimensional data, improving the visualization and classification accuracy compared to other standard dimensionality-reduction algorithms. Finally, we employed deep-learning-based CNN to classify the reduced and improved HSI intra- and inter-band relationship-feature vector. The evaluation performance of 95.21% accuracy and 6.2% test loss proved the superiority of the proposed model compared to other state-of-the-art DR reduction algorithms.

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Section: Related Workmentioning

confidence: 99%

Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network

Hossain

Miah

et al. 2023

Electronics

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“…For sequential data, such as text processing and sentiment analysis, NLP uses 1D convolutions. 1D convolutions are used in NLP applications to extract pertinent patterns and relationships from sentences, enabling models to understand semantic meaning and context [212]- [216]. Sentiment analysis for understanding customer opinions, named entity recognition to extract specific information from text, and text classification to classify news articles or product reviews are examples of NLP applications using 1D convolutions.…”

Section: Natural Language Processingmentioning

confidence: 99%

A Novel Levy Walk-Based Framework for Scheduling Power-Intensive Mobile Edge Computing Tasks

Younesi,

Fazli,

Ejlali

2023

Preprint

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A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

Younesi,

Ansari,

Fazli

et al. 2024

IEEE Access

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A Pioneering Approach of Hyperspectral Image Classification Employing the Cooperative Efforts of 3D, 2D and Depthwise Separable-1D Convolutions

Cited by 3 publications

References 27 publications

Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network

Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network

A Novel Levy Walk-Based Framework for Scheduling Power-Intensive Mobile Edge Computing Tasks

A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

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