IEEE Trans Image Process - SAR-based terrain classification using weakly supervised hierarchical Markov aspect models.

Tópicos

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Resumo

We introduce the hierarchical Markov aspect model (HMAM), a computationally efficient graphical model for densely labeling large remote sensing images with their underlying terrain classes. HMAM resolves local ambiguities efficiently by combining the benefits of quadtree representations and aspect models-the former incorporate multiscale visual features and hierarchical smoothing to provide improved local label consistency, while the latter sharpen the labelings by focusing them on the classes that are most relevant for the broader local image context. The full HMAM model takes a grid of local hierarchical Markov quadtrees over image patches and augments it by incorporating a probabilistic latent semantic analysis aspect model over a larger local image tile at each level of the quadtree forest. Bag-of-word visual features are extracted for each level and patch, and given these, the parent-child transition probabilities from the quadtree and the label probabilities from the tile-level aspect models, an efficient forwards-backwards inference pass allows local posteriors for the class labels to be obtained for each patch. Variational expectation-maximization is then used to train the complete model from either pixel-level or tile-keyword-level labelings. Experiments on a complete TerraSAR-X synthetic aperture radar terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training and test complexity. Keyword-level training greatly reduces the cost of providing training data with little loss of accuracy relative to pixel-level training.

Resumo Limpo

introduc hierarch markov aspect model hmam comput effici graphic model dens label larg remot sens imag under terrain class hmam resolv local ambigu effici combin benefit quadtre represent aspect modelsth former incorpor multiscal visual featur hierarch smooth provid improv local label consist latter sharpen label focus class relev broader local imag context full hmam model take grid local hierarch markov quadtre imag patch augment incorpor probabilist latent semant analysi aspect model larger local imag tile level quadtre forest bagofword visual featur extract level patch given parentchild transit probabl quadtre label probabl tilelevel aspect model effici forwardsbackward infer pass allow local posterior class label obtain patch variat expectationmaxim use train complet model either pixellevel tilekeywordlevel label experi complet terrasarx synthet apertur radar terrain map pixellevel ground truth show hmam accur effici provid signific better result compar singlescal aspect model modest increas train test complex keywordlevel train great reduc cost provid train data littl loss accuraci relat pixellevel train

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