Jiang et al., 2014 - Google Patents
Focal-test-based spatial decision tree learningJiang et al., 2014
- Document ID
- 4600852792150629231
- Author
- Jiang Z
- Shekhar S
- Zhou X
- Knight J
- Corcoran J
- Publication year
- Publication venue
- IEEE Transactions on Knowledge and Data Engineering
External Links
Snippet
Given learning samples from a raster data set, spatial decision tree learning aims to find a decision tree classifier that minimizes classification errors as well as salt-and-pepper noise. The problem has important societal applications such as land cover classification for natural …
- 238000003066 decision tree 0 title abstract description 44
Classifications
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- 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
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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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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- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
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- G06—COMPUTING; CALCULATING; COUNTING
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