Cargando…
Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization
The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283000/ https://www.ncbi.nlm.nih.gov/pubmed/35845897 http://dx.doi.org/10.1155/2022/6781740 |
_version_ | 1784747236972822528 |
---|---|
author | Subba Reddy, Tatireddy Harikiran, Jonnadula Enduri, Murali Krishna Hajarathaiah, Koduru Almakdi, Sultan Alshehri, Mohammed Naveed, Quadri Noorulhasan Rahman, Md Habibur |
author_facet | Subba Reddy, Tatireddy Harikiran, Jonnadula Enduri, Murali Krishna Hajarathaiah, Koduru Almakdi, Sultan Alshehri, Mohammed Naveed, Quadri Noorulhasan Rahman, Md Habibur |
author_sort | Subba Reddy, Tatireddy |
collection | PubMed |
description | The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy. |
format | Online Article Text |
id | pubmed-9283000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92830002022-07-15 Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization Subba Reddy, Tatireddy Harikiran, Jonnadula Enduri, Murali Krishna Hajarathaiah, Koduru Almakdi, Sultan Alshehri, Mohammed Naveed, Quadri Noorulhasan Rahman, Md Habibur Comput Intell Neurosci Research Article The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy. Hindawi 2022-07-07 /pmc/articles/PMC9283000/ /pubmed/35845897 http://dx.doi.org/10.1155/2022/6781740 Text en Copyright © 2022 Tatireddy Subba Reddy et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Subba Reddy, Tatireddy Harikiran, Jonnadula Enduri, Murali Krishna Hajarathaiah, Koduru Almakdi, Sultan Alshehri, Mohammed Naveed, Quadri Noorulhasan Rahman, Md Habibur Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization |
title | Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization |
title_full | Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization |
title_fullStr | Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization |
title_full_unstemmed | Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization |
title_short | Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization |
title_sort | hyperspectral image classification with optimized compressed synergic deep convolution neural network with aquila optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283000/ https://www.ncbi.nlm.nih.gov/pubmed/35845897 http://dx.doi.org/10.1155/2022/6781740 |
work_keys_str_mv | AT subbareddytatireddy hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization AT harikiranjonnadula hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization AT endurimuralikrishna hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization AT hajarathaiahkoduru hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization AT almakdisultan hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization AT alshehrimohammed hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization AT naveedquadrinoorulhasan hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization AT rahmanmdhabibur hyperspectralimageclassificationwithoptimizedcompressedsynergicdeepconvolutionneuralnetworkwithaquilaoptimization |