Object-based convolutional neural network for land use classification
US10922589B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 10, 2018 |
| Grant date | Feb 16, 2021 |
| Priority date | — |
| Expiry date | Jun 7, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30184
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An object-based convolutional neural network (OCNN) method and system for urban land use classification from VFSR imagery are described. In the OCNN, segmented objects consisting of linearly shaped objects (LS-objects) and other general objects (G-objects), are utilized as functional units. The G-objects are precisely identified and labelled through a single large input window (128×128) CNN with a deep (eight-layer) network to perform a contextual object-based classification. Whereas the LS-objects are each distinguished accurately using a range of small input window (48×48) CNNs with less deep (six-layer) networks along the objects' lengths through majority voting. The locations of the input image patches for both CNN networks are determined by considering both object geometry and its spatial anisotropy, such as to accurately classify the objects into urban land use classes.
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