Name
Title | ||
|---|---|---|
Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images |
Abstract | ||
|---|---|---|
Markov models represent a wide and general family of stochastic models for the temporal and spatial dependence properties associated to 1-D and multidimensional random sequences or random fields. Their applications range over a wide variety of subareas of the information and communication technology (ICT) field, including networking, automation, speech processing, genomic-sequence analysis, or image processing. Focusing on the applicative problem of land-cover mapping from very-high-resolution (VHR) remote sensing images, which is a relevant problem in many applications of environmental monitoring and natural resource exploitation, Markov models convey a great potential, thanks to their capability to effectively describe and incorporate the spatial information associated with image data into an image-classification process. In this framework, the main ideas and previous work about Markov modeling for VHR image classification will be recalled in this paper and processing results obtained through recent methods proposed by the authors will be discussed. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/JPROC.2012.2211551 | Proceedings of the IEEE |
Keywords | Field | DocType |
Markov processes,geophysical image processing,geophysical techniques,image classification,remote sensing,terrain mapping,ICT field,Markov modeling,Markov random fields,VHR image classification,communication technology,genomic-sequence analysis,image analysis,image-classification process,information technology,land-cover mapping,multidimensional random sequences,natural resource exploitation,random fields,spatial-contextual information,speech processing,stochastic models,very-high-resolution remote sensing images,Data fusion,Markov models,Markov random fields,land-cover mapping,remote sensing image classification | Spatial analysis,Speech processing,Spatial dependence,Markov process,Random field,Markov model,Remote sensing,Image processing,Artificial intelligence,Contextual image classification,Geography,Machine learning | Journal |
Volume | Issue | ISSN |
101 | 3 | 0018-9219 |
Citations | PageRank | References |
64 | 1.94 | 67 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Moser | 1 | 919 | 76.92 |
| Serpico, S.B. | 2 | 560 | 48.52 |
| Jon Atli Benediktsson | 3 | 4064 | 251.17 |