Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection

Can, Gulcan
Firat, Orhan
Yarman Vural, Fatoş Tunay
Developing a complex region detection algorithm that is aware of its contextual relations with several classes necessitates statistical frameworks that can encode contextual relations rather than simple rule-based applications or heuristics. In this study, we present a conditional random field (CRF) model that is generated over the results of a robust local discriminative classifier in order to reveal contextual relations of complex objects and land use/land cover (LULC) classes. The proposed CRF model encodes the contextual relation between the LULC classes and complex regions (airfields) as well as updates labels of the discriminative classifier and labels the complex region in a unified framework. The significance of the developed model is that it does not need any explicit parameters and/or thresholds along with heuristics or expert rules.


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A topological navigation algorithm that has the capability of mapping and localization based on visual contents is proposed. Keypoint detection and feature matching are conducted on spherical images to extract significant features among sequential frames. Robot movement direction is estimated based on historical angle differences of significant features to reach the final destination. The navigation process is supported with visual egocentric localization to gain simultaneous localization and mapping compet...
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Determining the process noise covariance matrix in Kalman filtering applications is a difficult task especially for estimation problems of the high-dimensional states where states like biases or system parameters are included. This study introduces a simplistic residual based adaptation method for the Unscented Kalman Filter (UKF), which is used for small satellite attitude estimation. For a satellite with gyros and magnetometers onboard, the proposed adaptive UKF algorithm estimates the attitude as well as...
Error Control of Multiple-Precision MLFMA
Kalfa, Mert; Ergül, Özgür Salih; Erturk, Vakur B. (Institute of Electrical and Electronics Engineers (IEEE), 2018-10)
We introduce and demonstrate a new error control scheme for the computation of far-zone interactions in the multilevel fast multipole algorithm when implemented within a multiple-precision arithmetic framework. The proposed scheme provides the optimum truncation numbers as well as the machine precisions given the desired relative error thresholds and the box sizes for the translation operator at all frequencies. In other words, unlike the previous error control schemes which are valid only for high-frequenc...
Citation Formats
G. Can, O. Firat, and F. T. Yarman Vural, “Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection,” 2012, vol. 7626, Accessed: 00, 2020. [Online]. Available: