Integrating Sentinel-1 Insar Coherence and Sentinel-2 Imagery for Detecting Built-up Features in the Hanoi Area
Main Article Content
Abstract
Hanoi is experiencing rapid urbanization, making the monitoring of land cover, particularly built-up areas, an increasingly important task. Remote sensing data has become a primary source of information for this purpose. In this study, the authors propose a method for identifying built-up features in the Hanoi area by integrating coherence maps derived from Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) with several remote sensing indices extracted from Sentinel-2 imagery. These indices include the Normalized Difference Built-up Index (NDBI), Brightness Index (BI), Colour Index (CI), and the modified Soil-Adjusted Vegetation Index (mSAVI). The proposed approach achieved a classification accuracy of 87%, with a Kappa coefficient of 0.7328. A comparative analysis was also conducted to evaluate the effectiveness of the integrated method against approaches using only Sentinel-1 or Sentinel-2 data. The results demonstrate that combining Sentinel-1 and Sentinel-2 enhances the ability to detect built-up features and supports more effective large-scale land cover monitoring.
Keywords:
Coherence mapping, spectral soil indices, integrating data, Sentinel-1, Sentinel-2.
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