Estimating the spatial extents of geospatial objects using hierarchical models

Yang, Yi; Newsam, Shawn

doi:10.1109/wacv.2012.6163040

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…These results can also be given as input to other algorithms so that more detailed labeling of the image can be produced. For example, the algorithm in [31] aims to estimate the spatial extents of complex geospatial objects that are composed of multiple land use and land cover classes. However, the method requires that at least a single known pixel is given as input for each object so that the procedure can be initialized and the model that was learned from multiple examples can compute its extent.…”

Section: E Resultsmentioning

confidence: 99%

Automatic Detection of Compound Structures by Joint Selection of Region Groups From a Hierarchical Segmentation

Akçay

Aksoy

2016

IEEE Trans. Geosci. Remote Sensing

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A challenging problem in remote sensing image analysis is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are composed of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of an unknown number of primitives in large scenes. The modeling process starts with a single example structure, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is formulated as the selection of multiple subsets of candidate regions from a hierarchical segmentation where each set of selected regions constitutes an instance of the example compound structure. The combinatorial selection problem is solved by the joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements. Experiments using very high spatial resolution images show that the proposed method can effectively localize an unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.

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Section: E Resultsmentioning

confidence: 99%

Automatic Detection of Compound Structures by Joint Selection of Region Groups From a Hierarchical Segmentation

Akçay

Aksoy

2016

IEEE Trans. Geosci. Remote Sensing

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“…That work, however, did not propose any appearance models, did not propose how those models would be learned in a semi-supervised manner, nor did it use ground truth spatial extents for evaluation. In [11], we developed the hierarchical model used in this work. The learning in that paper is completely supervised, however, using only manually labelled examples, and thus does not exploit gazetteers as a source of weakly labelled training data.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration

Yang

Newsam

2014

2014 22nd International Conference on Pattern Recognition

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We investigate how overhead imagery can be integrated with non-image geographic data to learn appearance models for geographic objects with minimal user supervision. While multi-modal data integration has been successfully applied in other domains, such as multimedia analysis, signiſcant opportunity remains for similar treatment of geographic data due to location being a simple yet powerful key for associating varying data modalities, and the growing availability of data annotated with location information either explicitly or implicitly.We present a speciſc instantiation of the framework in which overhead imagery is combined with gazetteers to compensate for a recognized deſciency: most gazetteers are incomplete in that the same latitude/longitude point serves as the bounding coordinates of the spatial extent of the indexed objects. We use a hierarchical object appearance model to estimate the spatial extents of these known object instances. The estimated extents can then be used to revise the gazetteers.A particularly novel contribution of our work is a semisupervised learning regime which incorporates weakly labelled training data, in the form of incomplete gazetteer entries, to improve the learned models and thus the spatial extent estimation.

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Ship detection from optical satellite images based on visual search mechanism

Yang

Gao

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Estimating the spatial extents of geospatial objects using hierarchical models

Cited by 3 publications

References 31 publications

Automatic Detection of Compound Structures by Joint Selection of Region Groups From a Hierarchical Segmentation

Automatic Detection of Compound Structures by Joint Selection of Region Groups From a Hierarchical Segmentation

Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration

Ship detection from optical satellite images based on visual search mechanism

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