# Satellite Image Compression Guided by Regions of Interest
## 3 Results  
The proposed system was implemented in Matlab, and the experiments were conducted using the images listed in Table 1. The parameters configured for the experiments can be summarized as follows:  
* Tile size: \(\delta=100\) ;
* Lossy compression rate: \(\beta=0.2\) bpp;
* Cloud detection threshold values: \(\tau_{1}=1500\), \(\tau_{2}=0.9\);
* Change detection threshold value: \(\tau_{3}=300\);
* Vessel detection threshold value: \(\tau_{4}=6\);
* Reference image for change detection: image 0;
* Morphological operations for vessel detection: 1 erode (length: 1 pixel), 1 dilate (length: 1 pixel);
* Morphological operations for change detection: 1 erode (length: 2 pixels), 1 dilate (length: 2 pixels). Some comments about the parameters can also be made. For \(\delta=100\), the input images have 169 tiles. It is also worth mentioning that \(\beta\) equal to 0.2 was chosen empirically, as this value results in an acceptable lossy representation of the non-relevant content without requiring a large amount of data. Furthermore, note that \(\tau_{2}=0.9\) implies that at least 90% of the tiles must be covered by clouds to be labeled as \"Cloudy\". Ideally, \(\tau_{2}\) would be equal to one. However, parts of the clouds cannot be detected due to brightness gradients in the clouds being higher with the higher values of \(\tau_{1}\). In that sense, giving a tolerance for \(\tau_{2}\) can improve the performance of the detection for some values of \(\tau_{1}\). Thus, both threshold values must be balanced. Another point to be mentioned is that the threshold value for the vessel detection algorithm was empirically set to \(\tau_{4}=6\), as in [12; 22]. Other values for \(\tau_{4}\) did not perform a better balance between PD and FAR. Gains in terms of image compression were computed using the NIR band as case of analysis. Results for the other bands can be considered analogous. Image 0 was selected to be used as reference image by the change detection algorithm. There was no particular reason for this choice other than that image 0 had the entire dryland region still as green, as shown by Figure 6a. Thus, the images containing changes in the green area could be detected, as illustrated by Figure 6b. The results for the 25 images are given in Table 2.  
First we discuss the results for image 0. Image 0 was selected to be used as the reference image by the change detection algorithm, so the surveillance image is the same as the reference image for the results shown in the first row of Table 2. Consequently, there were no changes to be detected. The reference image does not contain well defined vessels, but three small detections were found in the water region. Two of the three detections were in the same tile. Consequently, 2 of the 169 tiles were marked as ROI by the system, and almost the whole image was lossy compressed with \(r=\beta\). These detections can be due to small vessels, noise, or simply movement of water due to wind, for instance. This result can be verified in the second column of Table 2, where it can be seen that the final amount of information, measured in BPP, is close to \(\beta\). The gains achieved with the proposed method are shown by columns six and seven (calculated as defined in Section 2). To facilitate further comparisons, columns eight and nine show the BPP values achieved in the case of lossless compressing the images with the CCSDS image compressor and with JPEG2000--using the implementation provided by OpenJPEG [26]--respectively. The result of image 1 describes a scenario without clouds containing changes in the dry-land region, as illustrated by Figure 6b. The major changes in dry-land regions can be observed by comparing the upper-left quadrants of Figure 6a,b. Figure 7a shows the combined binary images resulting from the cloud detection algorithm, change detection algorithm, and vessel detection algorithm. The final labeled tiles are illustrated in Figure 7b.