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Pattern Recognition Letters
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A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
On the use of a hybrid approach to contrast endmember induction algorithms
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A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval
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IEEE Transactions on Information Technology in Biomedicine
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
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Morphological associative memories
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Pattern Recognition Letters
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We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (EIAs). Endmembers correspond to the elementary materials in the scene, so that the pixel spectra can be decomposed into a linear combination of endmember signatures. EIA search for points in the high dimensional space of pixel spectra defining a convex polytope, often a simplex, covering the image data. This paper introduces a dissimilarity measure between hyperspectral images computed over the image induced endmembers, proving that it complies with the axioms of a distance. We provide a comparative discussion of dissimilarity functions, and quantitative evaluation of their relative performances on a large collection of synthetic hyperspectral images, and on a dataset extracted from a real hyperspectral image. Alternative dissimilarity functions considered are the Hausdorff distance and robust variations of it. We assess the CBIR performance sensitivity to changes in the distance between endmembers, the EIA employed, and some other conditions. The proposed hyperspectral image distance improves over the alternative dissimilarities in all quantitative performance measures. The visual results of the CBIR on the real image data demonstrate its usefulness for practical applications.