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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Firat, O. Vural, F.T.Y. |
| Copyright Year | 2013 |
| Description | Author affiliation: Bilgisayar Muhendisligi Bolumu, Orta Dogu Teknik Univ., Ankara, Turkey (Firat, O.; Vural, F.T.Y.) |
| Abstract | The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features and the amount of labeled data in the dataset. In this study, we concentrated on representation learning using unlabeled remote sensing data and using these representations to recognize different objects which vary in complexity, characteristics and ground resolution. In the proposed framework, randomly sampled patches from remote sensing images are first used to train a single layer sparse-auto encoder in order to learn the most efficient representation for the dataset. These representations are appeared to be as gabor filters in various orientations and parameters, color co-occurrence and color filters and edge-detection filters. Subsequently, representations are used to extract features from target object based on convolution and pooling. Finally, extracted features are used to train a machine learning algorithm and classification performances are evaluated. The proposed method is tested on recognition of dispersal areas, taxi-routes, parking areas and airplanes which are all subparts of an airfield. Performance of the proposed method is competitive with currently used rulebased and supervised methods. |
| Sponsorship | IEEE Turkey Sect. SP Chapter |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 1074335 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467355629 |
| e-ISBN | 9781467355636 |
| e-ISBN | 9781467355612 |
| DOI | 10.1109/SIU.2013.6531525 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-04-24 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image color analysis Image edge detection representation learning sparse auto-encoders Filtering algorithms Feature extraction remote sensing Gabor filters unsupervised feature learning Artificial intelligence Remote sensing self-taught learning |
| Content Type | Text |
| Resource Type | Article |
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