Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network
The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly conv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/7/1550 |
| _version_ | 1827622648141578240 |
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| author | Yazeed Yasin Ghadi Adnan Ahmed Rafique Tamara al Shloul Suliman A. Alsuhibany Ahmad Jalal Jeongmin Park |
| author_facet | Yazeed Yasin Ghadi Adnan Ahmed Rafique Tamara al Shloul Suliman A. Alsuhibany Ahmad Jalal Jeongmin Park |
| author_sort | Yazeed Yasin Ghadi |
| collection | DOAJ |
| description | The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly convolutional neural networks (CNNs), have made outstanding accomplishments. Deep feature extraction from a CNN model is a frequently utilized technique in these approaches. Although CNN-based techniques have achieved considerable success, there is indeed ample space for improvement in terms of their classification accuracies. Certainly, fusion with other features has the potential to extensively improve the performance of distant imaging scene classification. This paper, thus, offers an effective hybrid model that is based on the concept of feature-level fusion. We use the fuzzy C-means segmentation technique to appropriately classify various objects in the remote sensing images. The segmented regions of the image are then labeled using a Markov random field (MRF). After the segmentation and labeling of the objects, classical and CNN features are extracted and combined to classify the objects. After categorizing the objects, object-to-object relations are studied. Finally, these objects are transmitted to a fully convolutional network (FCN) for scene classification along with their relationship triplets. The experimental evaluation of three publicly available standard datasets reveals the phenomenal performance of the proposed system. |
| first_indexed | 2024-03-09T11:29:38Z |
| format | Article |
| id | doaj.art-8dc6b75742964a42bcb6869a90043d88 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:29:38Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8dc6b75742964a42bcb6869a90043d882023-11-30T23:55:37ZengMDPI AGRemote Sensing2072-42922022-03-01147155010.3390/rs14071550Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional NetworkYazeed Yasin Ghadi0Adnan Ahmed Rafique1Tamara al Shloul2Suliman A. Alsuhibany3Ahmad Jalal4Jeongmin Park5Department of Computer Science and Software Engineering, Al Ain University, Al Ain 15551, United Arab EmiratesDepartment of Computer Science, Air University, Islamabad 44000, PakistanDepartment of Humanities and Social Science, Al Ain University, Al Ain 15551, United Arab EmiratesDepartment of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Computer Science, Air University, Islamabad 44000, PakistanDepartment of Computer Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si 15073, KoreaThe latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly convolutional neural networks (CNNs), have made outstanding accomplishments. Deep feature extraction from a CNN model is a frequently utilized technique in these approaches. Although CNN-based techniques have achieved considerable success, there is indeed ample space for improvement in terms of their classification accuracies. Certainly, fusion with other features has the potential to extensively improve the performance of distant imaging scene classification. This paper, thus, offers an effective hybrid model that is based on the concept of feature-level fusion. We use the fuzzy C-means segmentation technique to appropriately classify various objects in the remote sensing images. The segmented regions of the image are then labeled using a Markov random field (MRF). After the segmentation and labeling of the objects, classical and CNN features are extracted and combined to classify the objects. After categorizing the objects, object-to-object relations are studied. Finally, these objects are transmitted to a fully convolutional network (FCN) for scene classification along with their relationship triplets. The experimental evaluation of three publicly available standard datasets reveals the phenomenal performance of the proposed system.https://www.mdpi.com/2072-4292/14/7/1550CNN modelFCNHaralick textureparallel fusionremote sensingspectral-spatial features |
| spellingShingle | Yazeed Yasin Ghadi Adnan Ahmed Rafique Tamara al Shloul Suliman A. Alsuhibany Ahmad Jalal Jeongmin Park Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network Remote Sensing CNN model FCN Haralick texture parallel fusion remote sensing spectral-spatial features |
| title | Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network |
| title_full | Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network |
| title_fullStr | Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network |
| title_full_unstemmed | Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network |
| title_short | Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network |
| title_sort | robust object categorization and scene classification over remote sensing images via features fusion and fully convolutional network |
| topic | CNN model FCN Haralick texture parallel fusion remote sensing spectral-spatial features |
| url | https://www.mdpi.com/2072-4292/14/7/1550 |
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