Name
Title | ||
|---|---|---|
Improved segmentation of a series of remote sensing images by using a fusion MRF model |
Abstract | ||
|---|---|---|
Classifying segments and detection of changes in terrestrial areas are important and time-consuming efforts for remote-sensing image repositories. Some country areas are scanned frequently (e.g. year-by-year) to spot relevant changes, and several repositories contain multi-temporal image samples for the same area in very different quality and details. We propose a Multi-Layer Markovian adaptive fusion on Luv color images and similarity measure for the segmentation and detection of changes in a series of remote sensing images. We aim the problem of detecting details in rarely scanned remote sensing areas, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering based on a cross-image featuring, followed by multilayer MRF segmentation in the mixed dimensionality. On the base of the fused segmentation, the clusters of the single layers are trained by clusters of the mixed results. The improvement of this (partly) unsupervised method has been validated on remotely sensed image series. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CBMI.2013.6576571 | Content-Based Multimedia Indexing |
Keywords | Field | DocType |
Markov processes,geophysical image processing,image classification,image colour analysis,image fusion,image segmentation,pattern clustering,remote sensing,LUV color images,change detection classification,change segment classification,fusion MRF model,multilayer MRF segmentation,multilayer Markovian adaptive fusion,multitemporal image samples,partly supervised clustering,remote sensing image segmentation,remote-sensing image repositories,similarity measure,terrestrial areas,unsupervised clustering | Scale-space segmentation,Image fusion,Computer science,Remote sensing,Segmentation-based object categorization,Image processing,Image segmentation,Artificial intelligence,Region growing,Contextual image classification,Computer vision,Pattern recognition,Image texture | Conference |
ISSN | ISBN | Citations |
1949-3983 | 978-1-4799-0955-1 | 1 |
PageRank | References | Authors |
0.37 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sziranyi, T. | 1 | 395 | 44.76 |
| Maha Shadaydeh | 2 | 17 | 4.33 |