Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis
Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has incre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/1/86 |
| _version_ | 1818997405383655424 |
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| author | Rafael Pires de Lima Kurt Marfurt |
| author_facet | Rafael Pires de Lima Kurt Marfurt |
| author_sort | Rafael Pires de Lima |
| collection | DOAJ |
| description | Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification. |
| first_indexed | 2024-12-20T21:45:07Z |
| format | Article |
| id | doaj.art-b2d9238b6f7e4ef5b92e1fd3dd0f89eb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T21:45:07Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b2d9238b6f7e4ef5b92e1fd3dd0f89eb2022-12-21T19:25:42ZengMDPI AGRemote Sensing2072-42922019-12-011218610.3390/rs12010086rs12010086Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning AnalysisRafael Pires de Lima0Kurt Marfurt1School of Geosciences, University of Oklahoma, 100 East Boyd Street, RM 710, Norman, OK 73019, USASchool of Geosciences, University of Oklahoma, 100 East Boyd Street, RM 710, Norman, OK 73019, USARemote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.https://www.mdpi.com/2072-4292/12/1/86convolutional neural networkstransfer learningscene classification |
| spellingShingle | Rafael Pires de Lima Kurt Marfurt Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis Remote Sensing convolutional neural networks transfer learning scene classification |
| title | Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis |
| title_full | Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis |
| title_fullStr | Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis |
| title_full_unstemmed | Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis |
| title_short | Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis |
| title_sort | convolutional neural network for remote sensing scene classification transfer learning analysis |
| topic | convolutional neural networks transfer learning scene classification |
| url | https://www.mdpi.com/2072-4292/12/1/86 |
| work_keys_str_mv | AT rafaelpiresdelima convolutionalneuralnetworkforremotesensingsceneclassificationtransferlearninganalysis AT kurtmarfurt convolutionalneuralnetworkforremotesensingsceneclassificationtransferlearninganalysis |