## 3 Archaeological Feature Extraction from SAR: Methodological Approaches
### Single-Date Analysis  
The VHR SAR data proved able to depict urban areas in detail as well as different land use and land cover, providing useful information for investigations for feature extraction and cartographic applications. This capability of VHR SAR images allowed us to consider the use of these data also for archaeological feature extraction and mapping. The topic has already been approached using multi-temporal data analysis based on Stripmap data at a geometric resolution of 3 m (see Section 3.1). SAR remote sensing performance for archaeology would be better if Spotlight mode images were used. Moreover, these may enable us to achieve the performance and quality that is currently available from single-date optical VHR data processing. One of the main problems for archaeological feature extraction in SAR data is the complexity of the targets under investigation, the subtle return they provide, which means very low signal/noise ratio, and the fact that the visibility of archaeological marks is conditioned, as in the case of optical data, largely by numerous factors, for example (i) vegetation type and phenology for archaeological marks as well as (ii) soil type and superficial conditions in terms of humidity content for soil and damp marks. On the other hand, it is expected that the visibility and extraction of shadow marks should be conditioned less by the above mentioned factors as the SAR signal is sensitive to surface roughness. A compromise is needed to satisfactorily filter out noise, maintaining, at the same time, the subtle signal associated with archaeological features. In fact, speckle noise reduction may be generally beneficial to archaeological feature extraction, but the use of filtering to reduce noise could also filter out the target at the same time. Up to now, a number of effective speckle filtering algorithms have been developed and currently applied with a high degree of success in diverse fields of application. Nevertheless, it is important to consider that the performance of all the speckle filtering algorithms depends on the processed data set as well as on target characteristics. For the purposes of our analysis, we evaluated the performance obtained from the most popular speckle filtering algorithms. Two main types of SAR speckle filtering algorithms can be identified: (i) multi-look processing and (ii) image domain filtering techniques. More sophisticated approaches, such as those based on the wavelet filter, are not considered because they require numerous parameters to be set, which means that they cannot be promptly and easily applied to different geographical areas and archaeological features. In order to achieve a satisfactory compromise between the two opposite needs of smoothing noise and protecting detailed information, we adopted an approach based on both speckle reduction applying enhanced Lee, using a small window size (3 \(\times\) 3 up to 5 \(\times\) 5 pixels) and feature enhancement filtering (see Figure 2). It is important to note that the presence of speckle in SAR data makes the traditional edge detectors developed for optical images inefficient and feature identification and extraction extremely difficult. To cite an example, the processing of tonal information is not sufficient to reveal all the information stored in the SAR data. Several authors proposed robust detection operators to identify step edges in speckled images, but this methodology may be inadequate for archaeological features with very subtle returns [33, 34]. To cope with this issue, after noise removal using enhanced Lee, we also include a textural and morphological filter [35]. The former filter typology is more suited to extracting the complementary information available from the statistical indicators (such as mean, standard deviation coefficient of variation, autocorrelation for lags, _etc._).