Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 15, Issue -, Pages 7222-7234Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2022.3203195
Keywords
Coherence; Synthetic aperture radar; Time series analysis; Monitoring; Data mining; Buildings; Urban areas; Synthetic aperture radar; urban areas
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Funding
- Jiangsu Postdoctoral Grant
- National Natural Science Foundation of China [42001239]
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The aim of this work is to automatically extract and recognize urban change time series in sequences of SAR data. By combining SAR time-series segmentation and unsupervised classification, areas with the same urban change pattern can be identified. Experimental results show that the proposed approach is effective in characterizing temporal patterns of different types of intraurban changes.
The strong urbanization impetus of developing countries leads to various urbanization phenomena such as building constructions, reconstructions, and demolitions. It is desirable to monitor and recognize these intraurban changes by utilizing temporal and spatial information in an automatic way. This may be useful, for example, to timely update urban information databases. The aim of this work is, therefore, to automatically extract first, and further recognize, change time series in sequences of SAR data with high-frequency acquisition. Specifically, SAR time-series segmentation and unsupervised classification are combined together to recognize areas with the same urban change pattern, by fully exploiting both the temporal and spatial dimensions. Experimental results in a fast-growing Chinese city show that the proposed approach is effective and able to characterize temporal patterns due to different kinds of intraurban changes.
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