1,720,962 research outputs found
Fuzzy contextual classification of multisource remote sensing images
No full textOur objective has been to model satellite image classification
as a cognitive process, providing a procedure that mimics
the rich interaction of human activity in solving classification
problems. The key features of this approach are the definition of a
knowledge-based classification methodology designed to integrate
contextual information into a multisource classification scheme,
together with a fuzzy knowledge representation framework to
model the overall process in a form that closely resembles the
mental representation of human experts. An application for the
identification of the glacier equilibrium line in two different
zones of the Italian Alps has been developed to evaluate the
performance of our methodology in a real domain where class
discrimination requires the simultaneous use of contextual and
multisource information. Numerical results are provided and
compared with those obtained by a conventional classification
procedure. The advantages of the approach, as seen in the
experimental context, are examined
Approximate Reasoning and Multistrategy Learning for Multisource Remote Sensing Data Interpretation, in Information Processing for Remote Sensing
No full textA Neural Refinement Strategy For Fuzzy Dempster-Shafer Classifier of Multisource Remote Sensing Images
No full textA Neural Model for Fuzzy Dempster-Shafer Classifiers
No full textAbstractThis paper presents a supervised classification model integrating fuzzy reasoning and Dempster–Shafer propagation of evidence has been built on top of connectionist techniques to address classification tasks in which vagueness and ambiguity coexist. The salient aspect of the approach is the integration within a neuro-fuzzy system of knowledge structures and inferences for evidential reasoning based on Dempster–Shafer theory. In this context the learning task can be formulated as the search for the most adequate “ingredients” of the fuzzy and Dempster–Shafer frameworks such as the fuzzy aggregation operators, for fusing data from different sources and focal elements, and basic probability assignments, describing the contributions of evidence in the inference scheme. The new neural model allows us to establish a complete correspondence between connectionist elements and fuzzy and Dempster–Shafer ingredients, ensuring both a high level of interpretability, and transparency and high performance in classification. Experiments with simulated data show that the network can cope well with problems of different complexity. The experiments with real data show the superiority of the neural implementation with respect to the symbolic representation, and prove that the integration of the propagation of evidence provides better classification results and fuzzy reasoning within connectionist schema than those obtained by pure neuro-fuzzy models
A fuzzy neural Network for Knowledge-Based Refinement in Multisource Data Classification
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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