A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model
Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2013-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/5/10/5089 |
| _version_ | 1798024598559653888 |
|---|---|
| author | Apisit Eiumnoh Preesan Rakwatin Ratchawit Sirisommai Teerasit Kasetkasem |
| author_facet | Apisit Eiumnoh Preesan Rakwatin Ratchawit Sirisommai Teerasit Kasetkasem |
| author_sort | Apisit Eiumnoh |
| collection | DOAJ |
| description | Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF) model to simultaneously align two or more images and obtain a land cover map (LCM) of the scene. The expectation maximization (EM) algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP) criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced. |
| first_indexed | 2024-04-11T18:05:10Z |
| format | Article |
| id | doaj.art-502c5b630784412fa2f699dacde5f739 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:05:10Z |
| publishDate | 2013-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-502c5b630784412fa2f699dacde5f7392022-12-22T04:10:21ZengMDPI AGRemote Sensing2072-42922013-10-015105089512110.3390/rs5105089A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field ModelApisit EiumnohPreesan RakwatinRatchawit SirisommaiTeerasit KasetkasemTraditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF) model to simultaneously align two or more images and obtain a land cover map (LCM) of the scene. The expectation maximization (EM) algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP) criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.http://www.mdpi.com/2072-4292/5/10/5089joint land cover mapping and registrationMarkov random fieldoptimum classifiermean field theoryEM algorithm |
| spellingShingle | Apisit Eiumnoh Preesan Rakwatin Ratchawit Sirisommai Teerasit Kasetkasem A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model Remote Sensing joint land cover mapping and registration Markov random field optimum classifier mean field theory EM algorithm |
| title | A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model |
| title_full | A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model |
| title_fullStr | A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model |
| title_full_unstemmed | A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model |
| title_short | A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model |
| title_sort | joint land cover mapping and image registration algorithm based on a markov random field model |
| topic | joint land cover mapping and registration Markov random field optimum classifier mean field theory EM algorithm |
| url | http://www.mdpi.com/2072-4292/5/10/5089 |
| work_keys_str_mv | AT apisiteiumnoh ajointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel AT preesanrakwatin ajointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel AT ratchawitsirisommai ajointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel AT teerasitkasetkasem ajointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel AT apisiteiumnoh jointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel AT preesanrakwatin jointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel AT ratchawitsirisommai jointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel AT teerasitkasetkasem jointlandcovermappingandimageregistrationalgorithmbasedonamarkovrandomfieldmodel |