A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet
The accurate classification of aerial images is a crucial task in remote sensing, with applications ranging from land cover mapping and urban planning to disaster response and environmental monitoring. However, challenges such as limited labeled data, inherent data complexity, and high computational...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829594/ |
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| author | Muhammad Waqas Ahmed Moneerah Alotaibi Sultan Refa Alotaibi Dina Abdulaziz Alhammadi Asaad Algarni Ahmad Jalal Jeongho Cho |
| author_facet | Muhammad Waqas Ahmed Moneerah Alotaibi Sultan Refa Alotaibi Dina Abdulaziz Alhammadi Asaad Algarni Ahmad Jalal Jeongho Cho |
| author_sort | Muhammad Waqas Ahmed |
| collection | DOAJ |
| description | The accurate classification of aerial images is a crucial task in remote sensing, with applications ranging from land cover mapping and urban planning to disaster response and environmental monitoring. However, challenges such as limited labeled data, inherent data complexity, and high computational demands often hinder the performance of traditional methods. To address these challenges, we present an innovative framework that combines advanced segmentation techniques, diverse feature extraction methods, optimization algorithms, and deep learning. Our approach begins with novel Graph-cut Optimized Fuzzy GMM Segmentation (GC-GMM), ensuring precise object identification and boundary delineation. We employ Azimuthal Average Feature Extraction, Haar Wavelet Transform, and Maximally Stable Extremal Regions (MSER) to capture a rich set of features encompassing texture, frequency, and shape information. These features are fused and refined using Particle Swarm Optimization (PSO) to create a robust and informative representation. Leveraging the power of deep learning, a DenseNet architecture achieves superior classification accuracy based on the optimized feature set. This framework effectively tackles the limitations of previous methods by combining the strengths of diverse feature extraction techniques with deep learning capabilities. The use of optimization algorithms further enhances the discriminative power of the features, leading to improved classification accuracy. |
| format | Article |
| id | doaj-art-0e6ec609be2e4870be9a1ef0615174eb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0e6ec609be2e4870be9a1ef0615174eb2025-01-21T00:01:39ZengIEEEIEEE Access2169-35362025-01-01139372939010.1109/ACCESS.2025.352647610829594A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNetMuhammad Waqas Ahmed0https://orcid.org/0009-0003-6533-3068Moneerah Alotaibi1https://orcid.org/0000-0002-0074-8153Sultan Refa Alotaibi2Dina Abdulaziz Alhammadi3Asaad Algarni4Ahmad Jalal5https://orcid.org/0009-0000-8421-8477Jeongho Cho6https://orcid.org/0000-0001-5162-1745Department of Computer Science, Air University, Islamabad, PakistanDepartment of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi ArabiaDepartment of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi ArabiaDepartment of Computer Science, Air University, Islamabad, PakistanDepartment of Electrical Engineering, Soonchunhyang University, Asan, South KoreaThe accurate classification of aerial images is a crucial task in remote sensing, with applications ranging from land cover mapping and urban planning to disaster response and environmental monitoring. However, challenges such as limited labeled data, inherent data complexity, and high computational demands often hinder the performance of traditional methods. To address these challenges, we present an innovative framework that combines advanced segmentation techniques, diverse feature extraction methods, optimization algorithms, and deep learning. Our approach begins with novel Graph-cut Optimized Fuzzy GMM Segmentation (GC-GMM), ensuring precise object identification and boundary delineation. We employ Azimuthal Average Feature Extraction, Haar Wavelet Transform, and Maximally Stable Extremal Regions (MSER) to capture a rich set of features encompassing texture, frequency, and shape information. These features are fused and refined using Particle Swarm Optimization (PSO) to create a robust and informative representation. Leveraging the power of deep learning, a DenseNet architecture achieves superior classification accuracy based on the optimized feature set. This framework effectively tackles the limitations of previous methods by combining the strengths of diverse feature extraction techniques with deep learning capabilities. The use of optimization algorithms further enhances the discriminative power of the features, leading to improved classification accuracy.https://ieeexplore.ieee.org/document/10829594/Multi object classificationremote sensingfeature fusionobject detectionDenseNet-121 |
| spellingShingle | Muhammad Waqas Ahmed Moneerah Alotaibi Sultan Refa Alotaibi Dina Abdulaziz Alhammadi Asaad Algarni Ahmad Jalal Jeongho Cho A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet IEEE Access Multi object classification remote sensing feature fusion object detection DenseNet-121 |
| title | A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet |
| title_full | A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet |
| title_fullStr | A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet |
| title_full_unstemmed | A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet |
| title_short | A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet |
| title_sort | novel remote sensing recognition using modified gmm segmentation and densenet |
| topic | Multi object classification remote sensing feature fusion object detection DenseNet-121 |
| url | https://ieeexplore.ieee.org/document/10829594/ |
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