Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches

• Published in: Remote sensing of environment Vol. 215; pp. 313 - 329
• Main Authors: Dasgupta, Antara, Grimaldi, Stefania, Ramsankaran, R.A.A.J., Pauwels, Valentijn R.N., Walker, Jeffrey P.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 15.09.2018; Elsevier BV
• Subjects: Aerial photography; ANFIS; Artificial neural networks; Classification; COSMO-SkyMed; Disaster management; disaster preparedness; Emergency preparedness; Flood extent; Flood management; Flood mapping; Floods; Fuzzy logic; Fuzzy systems; GLCM; Image classification; Image enhancement; Mapping; monitoring; Neural networks; Neuro-fuzzy classification; Optics; Parameterization; Radar; Radar data; Radar imaging; Reduction; Reliability analysis; Remote sensing; SAR; Satellite observation; Satellites; Statistical analysis; Synthetic aperture radar; Texture; Texture optimization; Uncertainty; Neuro-fuzzy classification; SAR; Texture optimization; COSMO-SkyMed; Flood extent; Flood mapping; ANFIS; GLCM
• DOI: 10.1016/j.rse.2018.06.019
• ISSN: 0034-4257

Synthetic Aperture Radar (SAR) data are currently the most reliable resource for flood monitoring, though still subject to various uncertainties, which can be objectively represented with probabilistic flood maps. Moreover, the growing number of SAR satellites has increased the likelihood of observing a flood event from space through at least a single SAR image, but generalized methods for flood classification independent of sensor characteristics need to be developed, to fully utilize these images for disaster management. Consequently, a neuro-fuzzy flood mapping technique is proposed for texture-enhanced single SAR images. Accordingly, any SAR image is first processed to generate second-order statistical textures, which are subsequently optimized using a dimensionality reduction technique. The flood and non-flood classes are then modelled within a fuzzy inference system using Gaussian curves. Parameterization is achieved by training a neural network on the image through user-defined polygons. The results of the optimized texture-based neuro-fuzzy classification were compared against the performance of the SAR image alone and that of SAR enhanced with randomly selected texture features. This approach was tested for a COSMO-SkyMed SAR image at two validation sites, for which high resolution aerial photographs were available. An overall accuracy assessment using reliability diagrams demonstrated a reduction of 54.2% in the Weighted Root Mean Squared Error (WRMSE) values compared to the stand-alone use of SAR. WRMSE values estimated for the proposed method varied from 0.027 to 0.196. A fuzzy validation exercise was also proposed to account for the uncertainty in manual flood identification from aerial photography, resulting in fuzzy spatial similarity values ranging from 0.67 to 0.92, with higher values representing better performance. Results suggest that the proposed approach has demonstrated potential to improve operational SAR-based flood mapping. •An optimized texture-based SAR image enhancement technique is proposed.•A neurofuzzy classifier is used for probabilistic SAR-based flood mapping.•Improved flood classification accuracy using a single SAR image is demonstrated.•A fuzzy validation strategy is introduced for probabilistic flood map assessment.