Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization
T. Subba Reddy,J. Harikiran,Murali Krishna Enduri,K. Hajarathaiah,Sultan Almakdi,Mohammed Alshehri,Quadri Noorulhasan Naveed,Habibur Rahman +7 more
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This research analyzes how to accurately classify new HSI from limited samples with labels using a compressed synergic deep convolution neural network with Aquila optimization model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO).Abstract:Â
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.read more
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AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification
TL;DR: The developed AHA-AO was faster than the other feature selection models during the process of determining the relevant features and improved the performance and the speed of the overall deep learning models.
Journal ArticleDOI
Pixel-Wise Classification of Hyperspectral Images With 1D Convolutional SVM Networks
Mayar A. Shafaey,Farid Melgani,M. Saleem,Maryam N. Al-Berry,Hala Mousher Ebied,El-Sayed A. El-Dahshan,Mohamed F. Tolba +6 more
TL;DR: In this paper , a convolutional neural network based on one-dimensional support vector machine (SVM) convolution operations (1D-CSVM) was proposed for the analysis of hyperspectral images.
Journal ArticleDOI
A Comprehensive Survey on Aquila Optimizer
TL;DR: Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey-grabbing behavior of Aquila as discussed by the authors .
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Data Science in Healthcare Monitoring Under Covid-19 Detection by Extended Hybrid Leader-Based Compressed Neural Network
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A dual attention driven multiscale-multilevel feature fusion approach for hyperspectral image classification
TL;DR: In this article , a dual attention-based multiscale-multilevel ConvLSTM3D (DAMCL) was proposed to learn critical and valuable features from spectral and spatial dimensions.
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