Name
Title | ||
|---|---|---|
Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6947537 | IGARSS |
Keywords | Field | DocType |
geophysical techniques,radarsat-2 polarimetric sar,land cover,great toronto area,polsar data,dbn model,urban land cover,deep belief networks,land cover classification,remote sensing by radar,urban land use,urban mapping,image classification,deep learning,geophysical image processing,lulc classification,deep belief network(dbn),effective spatio-temporal mapping features,geographic information system,classification performance,polsar,restricted boltzmann machines(rbms),polarimetric radarsat-2 data,feature extraction,synthetic aperture radar | Data mining,Computer vision,Polarimetry,Computer science,Deep belief network,Remote sensing,Support vector machine,Artificial intelligence,Polarimetric sar,Deep learning,Land cover,Land use | Conference |
ISSN | Citations | PageRank |
2153-6996 | 2 | 0.39 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Lv | 1 | 84 | 9.09 |
| Yong Dou | 2 | 632 | 89.67 |
| Xin Niu | 3 | 56 | 11.39 |
| Jiaqing Xu | 4 | 57 | 6.44 |
| Baoliang Li | 5 | 2 | 0.39 |