The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
LSD: A Fast Line Segment Detector with a False Detection Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical and Contextual Model for Aerial Image Parsing
International Journal of Computer Vision
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Learning conditional random fields for classification of hyperspectral images
IEEE Transactions on Image Processing
Support vector random fields for spatial classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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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.