Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Proceedings |
| Kustantaja | IEEE |
| Sivut | 73-76 |
| Sivumäärä | 4 |
| ISBN (elektroninen) | 9781728121901 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 1 maaliskuuta 2020 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Tunis, Tunisia Kesto: 9 maaliskuuta 2020 → 11 maaliskuuta 2020 |
Conference
| Conference | 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 |
|---|---|
| Maa | Tunisia |
| Kaupunki | Tunis |
| Ajanjakso | 9/03/20 → 11/03/20 |
Tiivistelmä
In this work, we propose a novel classification approach based on dual-band one-dimensional Convolutional Neural Networks (1D-CNNs) for classification of multifrequency polarimetric SAR (PolSAR) data. The proposed approach can jointly learn from C- and L-band data and improve the single band classification accuracy. To the best of our knowledge, this is the first study that introduces 1D-CNNs to land use/land cover classification domain using PolSAR data. The proposed approach aims to achieve maximum classification accuracy by one-time training over multiple frequency bands with limited labelled data. Moreover, the proposed dual-band 1D-CNN approach yields a superior computational efficiency compared to the deep 2D-CNN based approaches. The performed experiments using AIRSAR PolSAR image over San Diego region at C- and L-bands have shown that the proposed approach is able to simultaneously learn from the C- and L-band SAR data and achieves an elegant classification performance with minimal complexity.