Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification
Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images make...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9291440/ |
| _version_ | 1798032017586126848 |
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| author | Ronald Tombe Serestina Viriri |
| author_facet | Ronald Tombe Serestina Viriri |
| author_sort | Ronald Tombe |
| collection | DOAJ |
| description | Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and learning complex. Effective image feature representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attains superior results than individual classifiers. This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results. |
| first_indexed | 2024-04-11T20:07:13Z |
| format | Article |
| id | doaj.art-8375e9d955184c62b7a659acda78357a |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T20:07:13Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-8375e9d955184c62b7a659acda78357a2022-12-22T04:05:18ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-011415516410.1109/JSTARS.2020.30442649291440Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene ClassificationRonald Tombe0Serestina Viriri1https://orcid.org/0000-0002-2850-8645School of Computer Science, University of KwaZuLu-Natal, Durban, South AfricaSchool of Computer Science, University of KwaZuLu-Natal, Durban, South AfricaRemote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and learning complex. Effective image feature representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attains superior results than individual classifiers. This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results.https://ieeexplore.ieee.org/document/9291440/Adaptive deep co-occurrence learningdeep feature extractionensemble learningmachine learningmultigrained forestsscene classification |
| spellingShingle | Ronald Tombe Serestina Viriri Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adaptive deep co-occurrence learning deep feature extraction ensemble learning machine learning multigrained forests scene classification |
| title | Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification |
| title_full | Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification |
| title_fullStr | Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification |
| title_full_unstemmed | Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification |
| title_short | Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification |
| title_sort | adaptive deep co occurrence feature learning based on classifier fusion for remote sensing scene classification |
| topic | Adaptive deep co-occurrence learning deep feature extraction ensemble learning machine learning multigrained forests scene classification |
| url | https://ieeexplore.ieee.org/document/9291440/ |
| work_keys_str_mv | AT ronaldtombe adaptivedeepcooccurrencefeaturelearningbasedonclassifierfusionforremotesensingsceneclassification AT serestinaviriri adaptivedeepcooccurrencefeaturelearningbasedonclassifierfusionforremotesensingsceneclassification |