Name
Title | ||
|---|---|---|
New hierarchical joint classification method for SAR-optical multiresolution remote sensing data |
Abstract | ||
|---|---|---|
In this paper, we develop a novel classification approach for multiresolution, multisensor (optical and synthetic aperture radar), and/or multiband images. Accurate and time-efficient classification methods arc particularly important tools to support rapid and reliable assessment of the ground changes. Given the huge amount and variety of data available currently from last-generation satellite missions, the main difficulty is to develop a classifier that can take benefit of multiband, multiresolution, and multisensor input imagery. The proposed method addresses the problem of multisensor fusion of SAR with optical data for classification purposes, and allows input data collected at multiple resolutions and additional multiscale features derived through wavelets to be fused. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | Multisensor,multiresolution remote sensing images,supervised classification,hierarchical Markov random fields |
Field | DocType | ISSN |
3D optical data storage,Image fusion,Synthetic aperture radar,Computer science,Remote sensing,Artificial intelligence,Classifier (linguistics),Wavelet,Computer vision,Satellite,Pattern recognition,Inverse synthetic aperture radar,Image resolution | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ihsen Hedhli | 1 | 7 | 2.19 |
| Gabriele Moser | 2 | 919 | 76.92 |
| Sebastiano B. Serpico | 3 | 749 | 64.86 |
| Josiane Zerubia | 4 | 2032 | 232.91 |