3.9
Challenges of SAR image processing
The difficulties faced during the interpretation of radar images are considered in this section. Most of these challenges are specific to SAR products. Geometric distortions, however, exist also in remotely-sensed optical imagery.
3.9.1 Speckle noise
The speckle, intrinsically present as a salt-and-pepper effect on SAR images, leads to wrongly-classified areas, especially for pixel-based classification methods which are misled by the noisy nature of these images into producing many false-alarms and false- detections (Melrose et al., 2012). The speckle noise arises in SAR products when the backscatter from different neighbouring targets is added up into the same resolution cell in the image, creating a granular pattern (Reddy, 2006). That is why, SAR images require a speckle-filtering to reduce the noise prior to any image interpretation.
3.9.2 Geometric distortions
Shadow and layover are caused by the side-looking nature of radars, and exist mainly in SAR images of urban areas due to tall buildings and in mountains (Mason et al., 2014). The flood does not affect the backscatter in shadowed areas (Giustarini et al., 2013), and thus these dark areas might need to be masked out prior to the flood mapping process to avoid introducing more false-positives.
3.9.2.1 Shadow
Shadow occurs essentially in mountain backslopes facing away from the SAR instru- ment, and in the areas behind elevated objects in general, which the radar beam cannot reach (Ulaby et al., 2014). It therefore appears dark on the SAR image since no echo is reflected back to the sensor. Parameters such as the SAR look angle and the look direction, besides the topography of the terrain feature illuminated, provoke the radar shadow. The shadowing effect increases, the steeper the slope is, and also as the look angle grows from the near range to the far range (Lillesand et al., 2008).
3.10. Conclusion 38
3.9.2.2 Layover
Layover is a common distortion in SAR images, where steep terrains look as if they are leaning towards the radar’s ground track. Radars being side-looking distance measuring instruments, the top of an elevated object is in a closer range to the radar’s antenna than its valley (Ulaby et al., 2014). This results in the echoed backscatter from higher points in the mountain reaching the sensor first. In contrast to shadows, the layover effect is more pronounced in the nearer range (Lillesand et al., 2008).
3.9.3 Soil moisture and wind-induced roughness
Smooth water surfaces act as specular reflectors returning a very low energy back to the radar sensor. As a result, the detection of floods is made straightforward by looking for areas with a low reflection. Nonetheless, difficulties arise from various environmental parameters impacting the intensity of the backscatter, which make the discrimination between water and land a complex operation. An example is given by the increase in the backscatter over land due to moist soils, and over water bodies caused by wind-induced roughness (Refice et al., 2013).
3.10
Conclusion
This chapter showed the characteristics of SAR that make it the ideal sensor to map the floods. It also went in depth about the properties that differentiate one SAR system from another (frequency, incidence angle, revisit time, and acquisition modes). The fundamental advantage of SAR instruments over optical ones is in the consistent observation of the Earth’s surface uninterrupted by cloudy conditions or by the absence of sunlight. However, some features intrinsic to radar data like the speckle noise, as well as the geometric peculiarities of SAR products (shadow and layover), render the classification and segmentation of these images more complex.
In the next chapter, the literature on flood mapping from SAR images will be systemat- ically reviewed, besides the techniques commonly used to pre-process and post-process
3.10. Conclusion 39
the data. Flood mapping methods are initially separated into those that delineate the extent of the inundation and the ones concerned with the estimation of the water level. In each of these two sections, papers relying on the same algorithm to derive these flood features will then be explained in the same subsection.
Chapter 4
Review of flood mapping
methods
In this chapter, a literature review was conducted on the mapping of floods using SAR images, by classifying the existing methods according to the technique used. The extrac- tion from SAR images of both the flood extent and the water depth features is covered. Besides, a few flood extent mapping methods applied on optical images acquired by different satellites are also listed. The inundation mapping techniques discussed can also be divided according to several other criteria. For instance, some flood mapping studies focus only on a single type of land cover over which the water is delineated (open areas, vegetation, or urban settlements). Other more robust flood mapping techniques with no supervision at all can also be distinguished. These fully-automated methods give the possibility to detect the flood in real-time and in a critical situation, and can ensure a successful flood mapping in an operational context. Moreover, after studying the flood mapping techniques in the literature, weaknesses to tackle were identified and a conclusion is drawn about the gaps that will be filled in the rest of this PhD thesis.