Karaca, 2021 - Google Patents
Spatial aware probabilistic multi-kernel collaborative representation for hyperspectral image classification using few labelled samplesKaraca, 2021
- Document ID
- 12909958379428135582
- Author
- Karaca A
- Publication year
- Publication venue
- International Journal of Remote Sensing
External Links
Snippet
Spatial-spectral hyperspectral image classification methods have gained increasing attention over the past decade. Although most of the methods achieve high performances in classification, they may give unreliable performances under some conditions. One of the …
- 230000004927 fusion 0 abstract description 35
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
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- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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- G—PHYSICS
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- G—PHYSICS
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- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
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