Abstract
Abstract. A classifier based on Bayesian theory and Markov random field (MRF) is presented to classify the active microwave and passive optical remote sensing data, which have demonstrated their respective advantages in inversion of surface soil moisture content. In the method, the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. And the model is validated for the necessities of integration of TM and ASAR, it shows that, the total precision of classification in this paper is 89.4%. Comparing with the classification with single TM, the accuracy increase 11.5%, illustrating that synthesis of active and passive optical remote sensing data is efficient and potential in classification.
Highlights
A range of remotely sensed data from sensors differing in terms of their spectral, spatial, and temporal resolution is widely available
We have developed a new classification model for multisource data based on the Markov Random Field (MRF) and Bayesian theory
A new classification model for active and passive remote sensing data is developed in this paper
Summary
A range of remotely sensed data from sensors differing in terms of their spectral, spatial, and temporal resolution is widely available. Integration of optical and microwave remote sensing in classification is attracting increasing attention, Reference (jia et al, 1995) used modified Bayesian Network to classify the Landsat TM and Aircraft SAR images, and found the precision of the classification by fusion TM and ASR are 20% higher than the single TM. The decision level fusion of TM and SAR images was applied to classification (Solberg, 1994), and further improved by adding the Markov random field; Storvik (Storvik, 2005) proposed Bayesian network to classify the multisource remote sensing with different spatial resolution and get an accuracy of 88.7%. We have developed a new classification model for multisource data based on the Markov Random Field (MRF) and Bayesian theory.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
