Name
Title | ||
|---|---|---|
Improving Unsupervised Flood Detection With Spatio-Temporal Context On Hj-1b Ccd Data |
Abstract | ||
|---|---|---|
The study of flood detection is significant to human life and social economy. In this paper, a completely unsupervised flood detection approach is presented, which combines spatio-temporal context and histogram thresholding. A global thresholding algorithm can be used in most of the cases to distinguish flood from non-flood pixels, but it may not distinguish local grey-level changes when the method is unsupervised. In this work, we introduce a kind of local context information to improve the results. A statistical model is used to establish the spatial relationships between each pixel and its surrounding regions, then a confidence map is computed. If the context structure changes significantly, the pixel is then considered potentially abnormal. Experimental investigations performed on HJ-1B CCD data from Northeast China during large-scale flooding in August 2013 showed higher precision of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7730147 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Flood detection, anomaly detection, spatio-temporal context, histogram thresholding, HJ CCD data | Data mining,Histogram,Computer science,Remote sensing,Artificial intelligence,Thresholding,Computer vision,Statistical model,Pixel,Temporal context,Potentially abnormal,Statistical classification,Flood myth | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyi Liu | 1 | 1 | 1.37 |
| Jiancheng Li | 2 | 0 | 0.68 |
| Hichem Sahli | 3 | 475 | 65.19 |
| Yu Meng | 4 | 1 | 1.42 |
| Qingqing Huang | 5 | 0 | 1.01 |