# Mass Processing of Sentinel-1 Images for
## 3 Methods
### Recurrent Targets Analysis  
A framework in the context of maritime surveillance to identify so-called recurrent detections (or targets), defined as those detections that appear in the same location in different acquisitions, has been presented in [38]. This framework exploits the availability of multiple images of the same area acquired at different times, and it has been applied in this study. Identification of recurrent maritime detections is important because, often, they are the result of image artefacts (ambiguities from strong land-based scatterers) and are, therefore, false alarms. In other cases, they indicate real fixed structures that can also be classified as false alarms (e.g., small unmapped islets or port structures) or structures that are undesired as targets in some maritime surveillance applications (e.g., buoys or oil platforms). As mentioned in Section 3.1, SUMO flags the detections that are deemed to be azimuth ambiguities in an individual image based on the deterministic distance between such an ambiguity and its originating target. However, SUMO will fail to flag azimuth ambiguities produced by strong scatterers outside the image and does not attempt to flag range ambiguities. For such ambiguities caused by fixed structures, this recurrence framework is very valuable. Of all the ship detections in this study, 20% have been identified as recurrent, and 25% of those have been categorized as ambiguities (see Section 4.4); these high numbers underline the added value this analysis can bring to SAR-based maritime surveillance. For completeness, an abridged description of the framework is presented here (see [38] for more details). Figure 4 shows the block diagram. SAR images are presented, one at a time, to the framework (input to diagram block A in Figure 4), and the objective is to produce a list of reliable non-recurrent detections (i.e., to exclude the recurrent detections) for the set of images (one of the outputs of block C in the figure), and to populate the databases that contain the recurrent targets (block D), the fixed structures (block F) and the ambiguities (block G). Depending on the application, only recurrent detections identified as ambiguities will be excluded from the list of detections, retaining targets like platforms; in other applications, all recurrent targets will be excluded. This choice is represented as the dashed lines providing input to block B. An image's metadata is needed to select the recurrent targets or ambiguities that are relevant to that image (block B), because the location of the ambiguities is deterministically governed by the observation geometry (azimuth direction, determined by relative orbit number and left/right antenna pointing) and by sensor parameters (Pulse Repetition Frequency, determined by mode and beam). The discrimination of recurrent targets in fixed structures or ambiguities (block E) is done using the idea that the geographic position of a fixed structure does not depend on the observation geometry or sensor parameters, whereas the geographic position of an ambiguity does depend on them. Consequently, fixed structures will be visible (and probably detected unless the structure shows very non-isotropic properties) in the same position for any observation geometry or set of sensor parameters, but ambiguities will only occur in a specific geographic position for one combination of geometry and sensor parameters. This framework simplifies somewhat in this study because all the images are of the same mode (Sentinel-1 IW). Therefore, the discrimination of recurrent targets in fixed structures or ambiguities is solely based on the observation geometry, which can be represented as a single number (the relative orbit number, an integer from 1 to 175) since Sentinel-1's antenna points always to the right.