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- W3132356275 abstract "Remote sensing scene change detection (SCD) is to detect whether and what changes have occurred in the semantic category of corresponding scenes for a long time at the semantic level. This can provide detailed land use/land cover change information for Urban planning and environmental monitoring. Previous studies take regular patches divided by uniform grid sampling as scene units. This may lead to mosaic phenomenon, and use fixed Window to extract features, ignoring the multi-scale features of ground objects, while extracting scene features. To solve the problems, the multi-scale irregular bag of visual features (MIBVF) framework is proposed for high spatial resolution (HSR) imagery SCD. In this paper, we integrate image classification of the physical characteristics from remote sensing data with the socio-economic attributes from open source geographic data. Road network data is used to preserve the geological significance and semantic integrity of urban scenes, and multi-scale window sampling is used to solve the problem of different object sizes. To confirm the feasibility of the proposed method, experiments with multi-temporal images of the Pudong area in Shanghai indicate that the proposed method achieves a clearly higher change detection accuracy than current state-of-the-art methods." @default.
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- W3132356275 date "2020-09-26" @default.
- W3132356275 modified "2023-09-24" @default.
- W3132356275 title "Urban Scenes Change Detection Based on Multi-Scale Irregular Bag of Visual Features for High Spatial Resolution Imagery" @default.
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- W3132356275 doi "https://doi.org/10.1109/igarss39084.2020.9323144" @default.
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