مشروع البحث: Domain Adaptation for Remote Sensing Using Deep Learning
| dc.contributor.advisor | Prof.D. Shawki Areibi, | |
| dc.contributor.advisor | Prof.D. Graham Taylor | |
| dc.date.accessioned | 2025-12-23T08:51:31Z | |
| dc.date.available | 2025-12-23T08:51:31Z | |
| dc.description | In this study, different techniques based on deep neural networks (DNNs) were developed and evaluated to solve the DA problem for remote sensing image classification in different settings. First, the single-source DA problem was addressed by finding invariant representations for both the source and the target. Denoising autoencoders (DAE) and domain-adversarial neural networks (DANN) were adopted to find these invariant representations. Results showed that both techniques were able to outperform traditional approaches, | |
| dc.description.abstract | Traditional machine learning (ML) techniques are often employed to perform complex pattern recognition tasks for remote sensing images, such as land-use classification. In order to obtain acceptable classification results, these techniques require sufficient training data to be available for every particular image. Obtaining training samples is challenging, particularly for near real-time applications. Therefore, past knowledge must be utilized to overcome the lack of training data in the current regime. This challenge is known as domain adaptation (DA), in which the training data (source) and the test data (target) are sampled from different domains. | |
| dc.identifier | 851 | |
| dc.identifier.uri | https://dspace.academy.edu.ly/handle/123456789/1859 | |
| dc.subject | Domain Adaptation for Remote | |
| dc.title | Domain Adaptation for Remote Sensing Using Deep Learning | |
| dspace.entity.type | Project | |
| project.endDate | 2020 | |
| project.funder.name | هندسة الالكترونية | |
| project.investigator | أحمد عبد العزيز عبدالله الشاملي | |
| project.startDate | 2019 |