Unlike periodic changes in natural cover, urban construction activities caused by urbanization show a distinctly non-periodic pattern in time. It is desirable to capture and recognize these changes, to timely update urban information databases among the others, by utilizing high-frequency observation of optical sensors or synthetic aperture radar (SAR) in an automatic way. To this aim, the primary task is to segment long time sequences and distinguish between changing and non-changing time segments. Unfortunately, urban building activities have different durations. Following up our previous work of monitoring urban building construction activities by using SAR coherent time series data [6], in this paper we focus on distinguishing among changed segments of different duration by a proposed semantic segmentation method based on LSTM autoencoders.
Semantic Segmentation and Recognition of Temporal Patterns in Urban SAR Sequences
Che M.;Vizziello A.;Gamba P.
2023-01-01
Abstract
Unlike periodic changes in natural cover, urban construction activities caused by urbanization show a distinctly non-periodic pattern in time. It is desirable to capture and recognize these changes, to timely update urban information databases among the others, by utilizing high-frequency observation of optical sensors or synthetic aperture radar (SAR) in an automatic way. To this aim, the primary task is to segment long time sequences and distinguish between changing and non-changing time segments. Unfortunately, urban building activities have different durations. Following up our previous work of monitoring urban building construction activities by using SAR coherent time series data [6], in this paper we focus on distinguishing among changed segments of different duration by a proposed semantic segmentation method based on LSTM autoencoders.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
