Name
Abstract | ||
|---|---|---|
In this paper, we propose a general framework to detect burned area using multiple partially occluded Moderate Resolution Imaging Spectroradiometer (MODIS) images. By treating each MODIS image as the superimposition of 3 layers: background, burned area, and cloud, we first apply a low-rank and sparse matrix decomposition technique known as Robust Principal Component Analysis (RPCA) to separate cloud from the other two components. Secondly, a joint sparsity-based representation for classification algorithm is employed to determine the burned area. We further robustify the algorithm by proposing a novel combined model that can separate and suppress the cloud information while simultaneously detecting the burn scar. Preliminary results using actual MODIS images clearly demonstrated the efficacy of the proposed technique. The probability of correct detection of burned area can reach more than 80% with less than 10% false alarm rate, even in an extremely cloudy day. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/GlobalSIP.2016.7905827 | 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) |
Keywords | Field | DocType |
RPCA,Low-rank,Joint-Sparse Representation,Detection,MODIS | Computer vision,Data modeling,Moderate-resolution imaging spectroradiometer,Superimposition,Computer science,Robust principal component analysis,Robustness (computer science),Artificial intelligence,Constant false alarm rate,Sparse matrix,Principal component analysis | Conference |
ISSN | ISBN | Citations |
2376-4066 | 978-1-5090-4546-4 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minh Dao | 1 | 121 | 11.14 |
| Chiman Kwan | 2 | 440 | 71.64 |
| Bulent Ayhan | 3 | 119 | 18.06 |
| Trac D. Tran | 4 | 1507 | 108.22 |