A Novel Deep Learning Framework With Meta-Heuristic Feature Selection for Enhanced Remote Sensing Image Classification
We propose a novel deep learning architecture, called XcelNet17, for image classification in remote sensing. Comprising fourteen convolutional and three fully connected layers, XcelNet17 outperforms several benchmark architectures available in the literature in terms of classification accuracy. Addi...
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creator | Ahmed, Bilal Akram, Tallha Rameez Naqvi, Syed Alsuhaibani, Anas Altherwy, Youssef N. Masud, Usman |
description | We propose a novel deep learning architecture, called XcelNet17, for image classification in remote sensing. Comprising fourteen convolutional and three fully connected layers, XcelNet17 outperforms several benchmark architectures available in the literature in terms of classification accuracy. Additionally, we present BA-ABC, a new hybrid feature selection algorithm that inherits the strengths of the Bat Algorithm (BA) and the Artificial Bee Colony (ABC) algorithm. Together these contributions significantly enhance the performance and accuracy of remote sensing image classification tasks. The proposed framework is thoroughly trained and verified using five benchmark datasets typically used for remote sensing image classification, namely AID, RSSCN7, SIRI-WHU, UC Merced, and WHU RS-19. Our simulation results suggest that in terms of classification accuracy, XcelNet17 outperforms most of the well established networks including AlexNet, VGG16, VGG19, ResNet50, and DarkNet19 by obtaining accuracy values in the range of 94.6% and 99.9%. Furthermore, the proposed features selection method, when integrated with XcelNet17, yields much improved classification accuracy in comparison to various benchmarks including WOA, GWO, BA, ABC, and ACO algorithms. For example, an 8% superior performance on WHU-RS19 dataset has been observed. The attained results are further validated by an in-depth statistical analysis. |
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Comprising fourteen convolutional and three fully connected layers, XcelNet17 outperforms several benchmark architectures available in the literature in terms of classification accuracy. Additionally, we present BA-ABC, a new hybrid feature selection algorithm that inherits the strengths of the Bat Algorithm (BA) and the Artificial Bee Colony (ABC) algorithm. Together these contributions significantly enhance the performance and accuracy of remote sensing image classification tasks. The proposed framework is thoroughly trained and verified using five benchmark datasets typically used for remote sensing image classification, namely AID, RSSCN7, SIRI-WHU, UC Merced, and WHU RS-19. Our simulation results suggest that in terms of classification accuracy, XcelNet17 outperforms most of the well established networks including AlexNet, VGG16, VGG19, ResNet50, and DarkNet19 by obtaining accuracy values in the range of 94.6% and 99.9%. Furthermore, the proposed features selection method, when integrated with XcelNet17, yields much improved classification accuracy in comparison to various benchmarks including WOA, GWO, BA, ABC, and ACO algorithms. For example, an 8% superior performance on WHU-RS19 dataset has been observed. The attained results are further validated by an in-depth statistical analysis.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3422368</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accuracy ; Algorithms ; Artificial bee colony ; bat algorithm ; Bees algorithm ; Benchmark testing ; Benchmarks ; bio-inspired feature selection ; Classification ; Classification algorithms ; CNN architecture ; Convolutional neural networks ; Datasets ; Deep learning ; Feature extraction ; Feature selection ; Heuristic methods ; Image classification ; Image enhancement ; Machine learning ; Remote sensing ; Statistical analysis ; Swarm intelligence</subject><ispartof>IEEE access, 2024, Vol.12, p.91974-91998</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0009-0001-5184-6810 ; 0000-0001-6954-926X ; 0000-0003-4578-3849 ; 0000-0002-9510-6638 ; 0000-0003-1067-4415 ; 0009-0006-4037-7445</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10583878$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Ahmed, Bilal</creatorcontrib><creatorcontrib>Akram, Tallha</creatorcontrib><creatorcontrib>Rameez Naqvi, Syed</creatorcontrib><creatorcontrib>Alsuhaibani, Anas</creatorcontrib><creatorcontrib>Altherwy, Youssef N.</creatorcontrib><creatorcontrib>Masud, Usman</creatorcontrib><title>A Novel Deep Learning Framework With Meta-Heuristic Feature Selection for Enhanced Remote Sensing Image Classification</title><title>IEEE access</title><addtitle>Access</addtitle><description>We propose a novel deep learning architecture, called XcelNet17, for image classification in remote sensing. Comprising fourteen convolutional and three fully connected layers, XcelNet17 outperforms several benchmark architectures available in the literature in terms of classification accuracy. Additionally, we present BA-ABC, a new hybrid feature selection algorithm that inherits the strengths of the Bat Algorithm (BA) and the Artificial Bee Colony (ABC) algorithm. Together these contributions significantly enhance the performance and accuracy of remote sensing image classification tasks. The proposed framework is thoroughly trained and verified using five benchmark datasets typically used for remote sensing image classification, namely AID, RSSCN7, SIRI-WHU, UC Merced, and WHU RS-19. Our simulation results suggest that in terms of classification accuracy, XcelNet17 outperforms most of the well established networks including AlexNet, VGG16, VGG19, ResNet50, and DarkNet19 by obtaining accuracy values in the range of 94.6% and 99.9%. Furthermore, the proposed features selection method, when integrated with XcelNet17, yields much improved classification accuracy in comparison to various benchmarks including WOA, GWO, BA, ABC, and ACO algorithms. For example, an 8% superior performance on WHU-RS19 dataset has been observed. The attained results are further validated by an in-depth statistical analysis.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial bee colony</subject><subject>bat algorithm</subject><subject>Bees algorithm</subject><subject>Benchmark testing</subject><subject>Benchmarks</subject><subject>bio-inspired feature selection</subject><subject>Classification</subject><subject>Classification algorithms</subject><subject>CNN architecture</subject><subject>Convolutional neural networks</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Feature extraction</subject><subject>Feature selection</subject><subject>Heuristic methods</subject><subject>Image classification</subject><subject>Image enhancement</subject><subject>Machine learning</subject><subject>Remote sensing</subject><subject>Statistical analysis</subject><subject>Swarm intelligence</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkU1v2zAMho1hA1q0_QXrQcDOzvRpycfAS9oA6Qo0G3YUZItKlTlWJjkd9u8nz8VQXSiQfF6SeIviI8ELQnD9edk0q91uQTHlC8YpZZV6V1xSUtUlE6x6_-Z_UdykdMD5qZwS8rJ4WaKv4QV69AXghLZg4uCHPVpHc4TfIf5EP_z4jB5gNOU9nKNPo-_QGsx4joB20EM3-jAgFyJaDc9m6MCiJziGcaoOadLaHM0eUNOblLzznZmA6-KDM32Cm9d4VXxfr7419-X28W7TLLdlR1U9lqwylpiWArSqZa0D4iyhojXMUQqGy1wAiSsmKs4sZ8xIW4PFzHYqA4JdFZtZ1wZz0Kfojyb-0cF4_S8R4l6bmE_qQTspCVGdULaueYt5noo5x4IL57DALmt9mrVOMfw6Qxr1IZzjkNfXDEuFVSUrnLvY3NXFkFIE938qwXryS89-6ckv_epXpm5nygPAG0IopqRifwGI-ZHj</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Ahmed, Bilal</creator><creator>Akram, Tallha</creator><creator>Rameez Naqvi, Syed</creator><creator>Alsuhaibani, Anas</creator><creator>Altherwy, Youssef N.</creator><creator>Masud, Usman</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Comprising fourteen convolutional and three fully connected layers, XcelNet17 outperforms several benchmark architectures available in the literature in terms of classification accuracy. Additionally, we present BA-ABC, a new hybrid feature selection algorithm that inherits the strengths of the Bat Algorithm (BA) and the Artificial Bee Colony (ABC) algorithm. Together these contributions significantly enhance the performance and accuracy of remote sensing image classification tasks. The proposed framework is thoroughly trained and verified using five benchmark datasets typically used for remote sensing image classification, namely AID, RSSCN7, SIRI-WHU, UC Merced, and WHU RS-19. Our simulation results suggest that in terms of classification accuracy, XcelNet17 outperforms most of the well established networks including AlexNet, VGG16, VGG19, ResNet50, and DarkNet19 by obtaining accuracy values in the range of 94.6% and 99.9%. 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subjects | Accuracy Algorithms Artificial bee colony bat algorithm Bees algorithm Benchmark testing Benchmarks bio-inspired feature selection Classification Classification algorithms CNN architecture Convolutional neural networks Datasets Deep learning Feature extraction Feature selection Heuristic methods Image classification Image enhancement Machine learning Remote sensing Statistical analysis Swarm intelligence |
title | A Novel Deep Learning Framework With Meta-Heuristic Feature Selection for Enhanced Remote Sensing Image Classification |
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