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Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data
| Content Provider | MDPI |
|---|---|
| Author | Liu, Hongying Luo, Ruyi Shang, Fanhua Meng, Xuechun Gou, Shuiping Hou, Biao |
| Copyright Year | 2020 |
| Description | Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods. |
| Starting Page | 1593 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12101593 |
| Journal | Remote Sensing |
| Issue Number | 10 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-05-17 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Metric Learning Semi-supervised Classification Manifold Regularization |
| Content Type | Text |
| Resource Type | Article |
