# Satellite Image Compression Guided by Regions of Interest
## 2 Materials and Methods
### Baseline Algorithm for Change Detection  
Detecting changes between multiple images of the same scene taken at different times has a large number of applications in diverse disciplines, including remote sensing, medical, civil infrastructure, and others [16]. Typically, for remote sensing applications, this would mean either that something interesting has significantly changed in the area or that a new type of object is in an area. Several strategies can be found in the literature aiming at detecting changes in optical satellite images [17] and in synthetic aperture radar (SAR) images [18; 19]. The challenge is that an image over a surveillance ground can look different every time the satellite passes, considering, for instance, the position of the sun, cloud shadows, satellite angle of incidence, satellite azimuth angle, and other factors. In urban areas, the vehicle traffic and parking lots would look different almost every time, which can be considered non-relevant content for some applications. However, there might still be interesting changes that do not involve cars, such as mass protests, fires, deforestation, and other events. In that sense, these contents can be detected by the on-board software and marked to be transmitted to the ground-station as ROI. A comparison of representative change extraction methods based on pixel analysis is given in [17], in which different methodologies are grouped as: algebraic and statistical analysis, feature space transformation, change classification, feature clustering, and deep neural network methods. Approaches combining more than one of the representative methods can also be found. The robustness of the models was evaluated considering the detection accuracy of the changed area, and also the accuracy of the non-changed area to point out a FAR. In this paper, we propose one methodology that fits into the group of algebraic and statistical analysis to perform the change detection step. Figure 3 shows the processing steps of the change detection algorithm. As illustrated by Figure 3, one low-pass filter is applied (2) to both the surveillance image (\(I_{sur}\)) and the reference image (\(I_{ref}\)) to reduce the influence from noise. Thus, the image (tile) is padded (1) before allowing the application of the filter to image borders. In this paper, the low-pass filter used is an averaging filter of window size 5 by 5 pixels. In the sequence, the differences in the image are computed from the averaged data by subtracting the reference image from the surveillance image (3). The absolute values of the  
Figure 2: ForSyDe model for the cloud detection algorithm using the synchronous model of computation (modified from [12]). In (**1**), one threshold operation (with the value \(\tau_{1}\)) is applied to each one of the input bands. In (**2**), the resulting binary matrices from each band are element-wise multiplied. Then, the elements are summed and divided in (**3**) according to the dimensions of the tiles. In (**4**), another threshold operation (with the value \(\tau_{2}\)) is performed to label each tile as “No-cloud” or “Cloudy”. pixels are calculated in the next step (4), since positive differences and negative differences are considered of interest. Then, the threshold operation is applied (5) to the absolute values of the image-difference. The threshold value, \(\tau_{3}\), might vary according to the satellite image sensors. Overall, the choice of a reference image acquired by the same sensor as the surveillance image will avoid complications in choosing the value of \(\tau_{3}\), and the use of images acquired in the same season, with same sun elevation and acquisition angle, will be beneficial. Finally, morphological operations are applied (6) to the binary output of the threshold block with the objective of reducing the false alarms related to single pixels (the circle \"morph. op.\"). Morphological operations can also avoid small alarming changes and deviations in georeferencing. In that sense, operations of erosion and dilation are applied.