Title :
Learning based data mining using compressed sensing
Author_Institution :
Fac. of Electr. Eng. & Comput. Sci., Univ. of Maribor, Maribor, Slovenia
Abstract :
This paper presents a categorization of SAR patches using supervised approach within a dictionary learning sparse representation framework. Dictionary learning algorithms represent matrix factorization of data matrix X as the product of Dictionary and sparse coefficients Z. The dictionary learning algorithm was implemented using well known K-SVD algorithm. The trained dictionaries were used for sparse representation and classification. Experimental results showed superior results for Dictionary-Learning Sparse Representation framework for categorization of SAR patches.
Keywords :
compressed sensing; geophysical techniques; geophysics computing; remote sensing by radar; synthetic aperture radar; K-SVD algorithm; SAR patches; compressed sensing; data matrix X; dictionary coefficient; dictionary learning algorithm represent matrix factorization; dictionary learning sparse representation framework; dictionary-learning sparse representation framework; learning based data mining; sparse classification; sparse coefficient; supervised approach; Accuracy; Compressed sensing; Databases; Dictionaries; Matching pursuit algorithms; Synthetic aperture radar; Training; Dictionary learning; Synthetic Aperture Radar; categorization;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
DOI :
10.1109/IGARSS.2014.6946763