Name
Title | ||
|---|---|---|
Unsupervised Object-Based Change Detection via a Weibull Mixture Model-Based Binarization for High-Resolution Remote Sensing Images. |
Abstract | ||
|---|---|---|
Object-based change detection (CD) is an effective method of identifying detailed changes in land features by contrastively observing the same areas of high-resolution remote sensing images at different times. Binarization is the important step in partitioning changed and unchanged classes in the unsupervised domain. We formulate a novel binarization technique based on the Weibull mixture model, w... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2017.2773118 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Remote sensing,Weibull distribution,Genetic algorithms,Mixture models,Parameter estimation,Spatial resolution,Robustness | Computer vision,Data set,Change detection,Similarity measure,Remote sensing,Weibull distribution,Robustness (computer science),Artificial intelligence,Estimation theory,Image resolution,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
15 | 1 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianjun Wu | 1 | 0 | 0.68 |
| Jian-Cheng Luo | 2 | 99 | 20.75 |
| jianwu fang | 3 | 11 | 3.91 |
| Jianghong Ma | 4 | 0 | 0.68 |
| Xueli Song | 5 | 0 | 0.34 |