Abstract
Building collapse arising from destructive earthquakes is often the primary cause of casualties and economic loss. Building damage assessment is one of the top priorities in earthquake emergency work. Quad-polarimetric synthetic aperture radar (PolSAR) data not only has the advantages of radar imaging being neither exposed to sunlight nor blocked by clouds, but also contains the most abundant information of the four polarimetric channels. In many cases, the texture feature even outperforms other kinds of features. The texture features of buildings include not only spatial texture but also frequency texture. We proposed a parameter called the sector texture feature of the Fourier amplitude spectrum (STFFAS) to describe frequency-domain texture features based on the Fourier amplitude spectrum for building damage recognition. Our experimental results show that the recognition performance of the frequency texture feature performs satisfactorily for building damage information extraction.
Original language | English |
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Title of host publication | IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium |
Publisher | IEEE |
Pages | 4658-4661 |
Number of pages | 4 |
ISBN (Electronic) | 979-8-3503-6032-5 |
ISBN (Print) | 979-8-3503-6033-2 |
DOIs | |
Publication status | Published (in print/issue) - 5 Sept 2024 |
Publication series
Name | IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium |
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Publisher | IEEE Control Society |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Economics
- Fourier transforms
- Frequency-domain analysis
- Buildings
- Earthquakes
- Geoscience and remote sensing
- Radar imaging
- PoISAR
- Earthquake
- Building damage
- Texture features
- Frequency domain
- PolSAR