@article {bnh-6266, title = {Flood mapping under vegetation using single SAR acquisitions}, journal = {Remote Sensing of Environment}, volume = {237}, year = {2020}, month = {02/2020}, abstract = {

Synthetic Aperture Radar (SAR) enables 24-hour, all-weather flood monitoring. However, accurate detection of inundated areas can be hindered by the extremely complicated electromagnetic interaction phenomena between microwave pulses, and horizontal and vertical targets. This manuscript focuses on the problem of inundation mapping in areas with emerging vegetation, where spatial and seasonal heterogeneity makes the systematic distinction between dry and flooded backscatter response even more difficult. In this context, image interpretation algorithms have mostly used detailed field data and reference image(s) to implement electromagnetic models or change detection techniques. However, field data are rare, and despite the increasing availability of SAR acquisitions, adequate reference image(s) might not be readily available, especially for fine resolution acquisitions. To by-pass this problem, this study presents an algorithm for automatic flood mapping in areas with emerging vegetation when only single SAR acquisitions and common ancillary data are available. First, probability binning is used for statistical analysis of the backscatter response of wet and dry vegetation for different land cover types. This analysis is then complemented with information on land use, morphology and context within a fuzzy logic approach. The algorithm was applied to three fine resolution images (one ALOS-PALSAR and two COSMO-SkyMed) acquired during the January 2011 flood in the Condamine-Balonne catchment (Australia). Flood extent layers derived from optical images were used as validation data, demonstrating that the proposed algorithm had an overall accuracy higher than 80\% for all case studies. Notwithstanding the difficulty to fully discriminate between dry and flooded vegetation backscatter heterogeneity using a single SAR image, this paper provides an automatic, data parsimonious algorithm for the detection of floods under vegetation.

}, keywords = {Flooded vegetation, Fuzzy logic, Inundation extent, SAR}, doi = {https://doi.org/10.1016/j.rse.2019.111582}, url = {https://www.sciencedirect.com/science/article/pii/S0034425719306029?dgcid=author}, author = {Stefania Grimaldi and Xu, J and Yuan Li and Jeffrey Walker} } @article {bnh-7976, title = {Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches}, journal = {Remote Sensing of Environment}, volume = {215}, year = {2018}, month = {09/2018}, pages = {313-329}, abstract = {

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.

}, keywords = {ANFIS, COSMO-SkyMed, Flood extent, Flood mapping, GLCM, Neuro-fuzzy classification, SAR, Texture optimization}, doi = {https://doi.org/10.1016/j.rse.2018.06.019}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0034425718302979?via\%3Dihub}, author = {Antara Dasgupta and Stefania Grimaldi and RAAJ Ramsankaran and Valentijn Pauwels and Jeffrey Walker} }