Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization.

Autor: Subba Reddy T; Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, Telangana, India Pin: 502313., Harikiran J; School of CSE, VIT-AP University, Vijayawada, Pin: 522237, Andhrapradesh, India., Enduri MK; Computer Science and Engineering, SRM University-AP, Amaravati, India., Hajarathaiah K; Computer Science and Engineering, SRM University-AP, Amaravati, India., Almakdi S; Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia., Alshehri M; Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia., Naveed QN; Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia., Rahman MH; Dept. of Computer Science and Engineering, Faculty of Engineering and Technology, Islamic University, Kushtia-7003, Bangladesh.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jul 07; Vol. 2022, pp. 6781740. Date of Electronic Publication: 2022 Jul 07 (Print Publication: 2022).
DOI: 10.1155/2022/6781740
Abstrakt: 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.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Tatireddy Subba Reddy et al.)
Databáze: MEDLINE
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