Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data
2014 IEEE Geoscience and Remote Sensing Symposium, 2014•ieeexplore.ieee.org
Urban land use and land cover (LULC) classification is one of the core applications in
Geographic Information Sys-tem (GIS). In this paper, a novel classification approach based
on Deep Belief Network (DBN) for detailed urban mapping is proposed. Deep Belief
Network (DBN) is a widely investigated and deployed deep learning model. By applying the
DBN model, effective spatio-temporal mapping features can be automatically extracted to
improve the classification performance. Six-date RADARSAT-2 Polarimetric SAR (PolSAR) …
Geographic Information Sys-tem (GIS). In this paper, a novel classification approach based
on Deep Belief Network (DBN) for detailed urban mapping is proposed. Deep Belief
Network (DBN) is a widely investigated and deployed deep learning model. By applying the
DBN model, effective spatio-temporal mapping features can be automatically extracted to
improve the classification performance. Six-date RADARSAT-2 Polarimetric SAR (PolSAR) …
Urban land use and land cover (LULC) classification is one of the core applications in Geographic Information Sys-tem(GIS). In this paper, a novel classification approach based on Deep Belief Network(DBN) for detailed urban mapping is proposed. Deep Belief Network (DBN) is a widely investigated and deployed deep learning model. By applying the DBN model, effective spatio-temporal mapping features can be automatically extracted to improve the classification performance. Six-date RADARSAT-2 Polarimetric SAR (PolSAR) data over the Great Toronto Area were used for evaluation. Experimental results showed that the proposed method can outperform SVM and contextual approaches using adaptive MRF.
ieeexplore.ieee.org
Showing the best result for this search. See all results
