# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Selected Studies of Combined Approaches
[49], [PERSON] et al. [51]. The ACM, also known as the snake algorithm [60], was, to the authors' knowledge, the first image segmentation algorithm designed for SAR data. It allows a certain amount of backscattering heterogeneity, while no smoothing across segment boundaries occurs. A smooth contour is favored by the inclusion of curvature and tension constraint. The algorithm spawns smaller snakes to represent multiple connected regions. The snake starts as a narrow strip moving along the course of a river channel, ensuring it contains only flooded pixels. Overall, it can deal with low signal-to-noise ratio. [PERSON] et al. [68] used ACM to map waterlines under vegetation. They started from known pure ocean pixels to map the active contour of open water and then to map the second active contour, which was the waterline beneath vegetation. Two radar signatures--the enhanced backscattering at C-band and the HH-VV phase difference at L-band--forced the ACM. Unlike the OO method, which aggregates objects from the bottom (pixel level) to the top, the segmentation in ACM requires seeding pixels, whose detection is difficult in an automated approach. In addition, similar to RGA, ACM cannot detect inundated areas isolated from a known water body. To assess flooding beneath vegetation, [PERSON] et al. [63] developed an image segmentation method using multi-temporal SAR images and utilized a microwave scattering model [78] that combines matrix doubling [30; 31] and the integral equation model (IEM) [31; 79] to interpret the object backscattering signatures. The image segmentation method dilutes and erodes multi-temporal SAR images using different window sizes. Then, an unsupervised classification is carried out at the pixel level. The object patches are formed based on connected pixels of the same class. Eventually, the trend of patch scattering can be compared with canonical values simulated by a scattering model to derive the flood map. [PERSON] et al. [47] proposed a fuzzy logic-based classifier to incorporate texture, context, and ancillary data (elevation, land cover, and so on), as an alternative to thresholding and segmentation methods. Eventually, they combined the segmentation and fuzzy logic classifier [69] by using backscattering change and backscattering during flooding as input for densely vegetated areas. The thresholds of the membership functions were fine-tuned with a scattering model. The use of image segmentation reduces the disturbance by speckle; the use of multi-temporal trend takes into account the progress of the water receding process, and the comparison with a scattering model soundsmore objective and location independent than a supervised classification or empirical thresholds. The adopted image segmentation may, however, reduce the inundation extent detail, and it does not guarantee the removal of speckle. Scattering models require detailed vegetation information (moisture, height, density, canopy particle orientation and size distribution, trunk height, and diameter) and ground soil information (moisture, roughness) as input; these data are heterogeneous in space and usually not available. [PERSON] et al. [33] and [PERSON] et al. [34] argued the advantages of utilizing the loss of coherence as the primary signature of flooding buildings over the enhancement of intensities [73], as has been briefly summarized in Sections 2.1, 3.1.4 and 3.1.6. To rule out false positives created by vegetated areas, [PERSON] et al. [35] first identified buildings by thresholding temporally averaged intensities using the HSBA method, in conjunction with (via logical OR) thresholding temporally averaged coherence. Then they masked out layover and shadowed areas by using the local incidence angle to prevent false alarms there. As stated in [80] both polarized intensities and coherence exhibit contrasting behaviors over built-up and vegetated areas.