Urban Classification Using Multi-temporal Sentinel-1 Data Based On Coherence Characteristics

The paper presents the method of urban classification using the coherence characteristics of pairs of SAR images observed at different times. Two scenes of Sentinel-1A VV and VH polarized on January 16, 2020, and January 28, 2020, in some central districts of Hanoi city were used experimentally in this study. The primary data processing steps included: (1) Creating the coherence image by using a pair of SAR interference images; (2) Processing coherence image by computing multi-look and geometric correction to UTM coordinate system; (3) Classification of the coherence image to urban/non-urban areas threshold method. The results showed that the urban extracted from the VH polarization image was better than the VV polarization image. The overall accuracy of classification achieved for VV and VH polarized images were 89% and 93%. Using SAR image pairs to classify urban areas that were not affected by weather conditions, showed good efficiency in managing and monitoring urban space in Vietnam cities