Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Proceedings |
| Publisher | IEEE |
| Pages | 73-76 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781728121901 |
| DOIs | |
| Publication status | Published - 1 Mar 2020 |
| Publication type | A4 Article in a conference publication |
| Event | 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Tunis, Tunisia Duration: 9 Mar 2020 → 11 Mar 2020 |
Conference
| Conference | 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 |
|---|---|
| Country | Tunisia |
| City | Tunis |
| Period | 9/03/20 → 11/03/20 |
Abstract
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.
ASJC Scopus subject areas
Keywords
- 1D Convolutional Neural Networks, land use/land cover classification, multifrequency classification, Polarimetric Synthetic Aperture Radar (PolSAR)