5,033 research outputs found
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
This paper proposes a novel framework for fusing multi-temporal,
multispectral satellite images and OpenStreetMap (OSM) data for the
classification of local climate zones (LCZs). Feature stacking is the most
commonly-used method of data fusion but does not consider the heterogeneity of
multimodal optical images and OSM data, which becomes its main drawback. The
proposed framework processes two data sources separately and then combines them
at the model level through two fusion models (the landuse fusion model and
building fusion model), which aim to fuse optical images with landuse and
buildings layers of OSM data, respectively. In addition, a new approach to
detecting building incompleteness of OSM data is proposed. The proposed
framework was trained and tested using data from the 2017 IEEE GRSS Data Fusion
Contest, and further validated on one additional test set containing test
samples which are manually labeled in Munich and New York. Experimental results
have indicated that compared to the feature stacking-based baseline framework
the proposed framework is effective in fusing optical images with OSM data for
the classification of LCZs with high generalization capability on a large
scale. The classification accuracy of the proposed framework outperforms the
baseline framework by more than 6% and 2%, while testing on the test set of
2017 IEEE GRSS Data Fusion Contest and the additional test set, respectively.
In addition, the proposed framework is less sensitive to spectral diversities
of optical satellite images and thus achieves more stable classification
performance than state-of-the art frameworks.Comment: accepted by TGR
Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level
We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes
Classification of sporting activities using smartphone accelerometers
In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging
sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach
Vote-boosting ensembles
Vote-boosting is a sequential ensemble learning method in which the
individual classifiers are built on different weighted versions of the training
data. To build a new classifier, the weight of each training instance is
determined in terms of the degree of disagreement among the current ensemble
predictions for that instance. For low class-label noise levels, especially
when simple base learners are used, emphasis should be made on instances for
which the disagreement rate is high. When more flexible classifiers are used
and as the noise level increases, the emphasis on these uncertain instances
should be reduced. In fact, at sufficiently high levels of class-label noise,
the focus should be on instances on which the ensemble classifiers agree. The
optimal type of emphasis can be automatically determined using
cross-validation. An extensive empirical analysis using the beta distribution
as emphasis function illustrates that vote-boosting is an effective method to
generate ensembles that are both accurate and robust
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