TY - JOUR
T1 - PolSARNet: A Deep Fully Convolutional Network for Polarimetric SAR Image Classification
AU - Mullissa, A.G.
AU - Persello, C.
AU - Stein, A.
PY - 2019/12
Y1 - 2019/12
N2 - Deep learning has successfully improved the classification accuracy of optical remote sensing images. Recent works attempted to transfer the success of these techniques to the microwave domain to classify Polarimetric SAR data. So far, most deep learning networks separate amplitude and phase as separate input images. In this article, we present a deep fully convolutional network that uses real-valued weight kernels to perform pixel-wise classification of complex-valued images. We evaluated the performance of this network by comparing it with support vector machine, Random Forest, complex-valued convolutional neural network (CV-CNN), and a network that uses amplitude and phase information separately as real channels. The evaluation was done on a quad-polarized AIRSAR image and a dual-polarimetric multitemporal Sentinel-1 data acquired over Flevoland, the Netherlands. The proposed method achieved higher accuracy compared to all other networks with the same architecture.
AB - Deep learning has successfully improved the classification accuracy of optical remote sensing images. Recent works attempted to transfer the success of these techniques to the microwave domain to classify Polarimetric SAR data. So far, most deep learning networks separate amplitude and phase as separate input images. In this article, we present a deep fully convolutional network that uses real-valued weight kernels to perform pixel-wise classification of complex-valued images. We evaluated the performance of this network by comparing it with support vector machine, Random Forest, complex-valued convolutional neural network (CV-CNN), and a network that uses amplitude and phase information separately as real channels. The evaluation was done on a quad-polarized AIRSAR image and a dual-polarimetric multitemporal Sentinel-1 data acquired over Flevoland, the Netherlands. The proposed method achieved higher accuracy compared to all other networks with the same architecture.
KW - ITC-ISI-JOURNAL-ARTICLE
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1109/JSTARS.2019.2956650
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2020/isi/persello_pol.pdf
U2 - 10.1109/JSTARS.2019.2956650
DO - 10.1109/JSTARS.2019.2956650
M3 - Article
VL - 12
SP - 5300
EP - 5309
JO - IEEE Journal of selected topics in applied earth observations and remote sensing
JF - IEEE Journal of selected topics in applied earth observations and remote sensing
SN - 1939-1404
IS - 12
M1 - 8936481
ER -