Abstract:
One- and multidimensional Markov models represent a general family of stochastic models for the dependence properties associated with random sequences or random fields in...Show MoreMetadata
Abstract:
One- and multidimensional Markov models represent a general family of stochastic models for the dependence properties associated with random sequences or random fields in many applications in the Information and Communication Technology (ICT) field, such as networking, automation, speech processing, genomic-sequence analysis, or image processing. Here, we focus on land cover mapping from very high-resolution remote-sensing images, which is an important problem in many environmental monitoring and natural resource management applications. In this framework, Markov random fields are of great importance. They allow the spatial information associated with image data to be described and effectively incorporated into image classification. The main ideas and previous work about Markov modeling for very high-resolution image classification are reviewed in the paper and processing results obtained through recent methods proposed by the authors are discussed.
Date of Conference: 12-14 September 2012
Date Added to IEEE Xplore: 13 December 2012
ISBN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Random Fields ,
- Remote Sensing ,
- Land Cover Classes ,
- Markov Random Field ,
- Remote Sensing Images ,
- Markov Random Field Model ,
- Multispectral Remote Sensing ,
- Image Classification ,
- Information And Communication Technologies ,
- Family Of Models ,
- Speech Processing ,
- Land Cover Map ,
- High-resolution Remote Sensing Images ,
- Urban Areas ,
- Support Vector Machine ,
- Probability Density Function ,
- Class Labels ,
- Potential Model ,
- Texture Features ,
- Positive Parameter ,
- Very High Resolution ,
- Maximum A Posteriori ,
- Extract Texture ,
- Multiscale Method ,
- Built-up Land ,
- Segmentation Map ,
- Multiple Sources Of Information ,
- Remote Sensing Data ,
- Test Pixel ,
- Urban Land
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Random Fields ,
- Remote Sensing ,
- Land Cover Classes ,
- Markov Random Field ,
- Remote Sensing Images ,
- Markov Random Field Model ,
- Multispectral Remote Sensing ,
- Image Classification ,
- Information And Communication Technologies ,
- Family Of Models ,
- Speech Processing ,
- Land Cover Map ,
- High-resolution Remote Sensing Images ,
- Urban Areas ,
- Support Vector Machine ,
- Probability Density Function ,
- Class Labels ,
- Potential Model ,
- Texture Features ,
- Positive Parameter ,
- Very High Resolution ,
- Maximum A Posteriori ,
- Extract Texture ,
- Multiscale Method ,
- Built-up Land ,
- Segmentation Map ,
- Multiple Sources Of Information ,
- Remote Sensing Data ,
- Test Pixel ,
- Urban Land