# Satellite Image Compression Guided by Regions of Interest  
[PERSON]  
1 KTH Royal Institute of Technology, 100 44 Stockholm, Sweden 1  
[PERSON]  
1 KTH Royal Institute of Technology, 100 44 Stockholm, Sweden 1  
[PERSON]  
2 Unibap AB, Kungsangsgatan 12, 753 22 Uppsala, Sweden 23 School of Innovation, Design and Engineering (IDT), Embedded Systems Division, Milardalen University, P.O. Box 883, 721 23 Vasteris, Sweden 3  
[PERSON]  
2 Unibap AB, Kungsangsgatan 12, 753 22 Uppsala, Sweden 2  
[PERSON]  
4 Saab AB, Olof Palmes Gata 17, 111 22 Stockholm, Sweden 4  
[PERSON]  
1 KTH Royal Institute of Technology, 100 44 Stockholm, Sweden 1  
for Space Data System (CCSDS). The CCSDS 122.0-B-2 Recommended Standard details an image compression algorithm that can be seen as a minimalist version of JPEG2000, in which a careful trade-off between compression performance and complexity has been made to make the compressor more suitable to be implemented in either hardware or software [4]. An alternative to the CCSDS image compressor is the JPEG-LS standard, presenting lower complexity and similar performance in terms of data compression, but this is limited to lossless and near-lossless compression [5]. Other general-purpose wavelet-based image compression algorithms capable of providing effective lossless and lossy compression can be found in [6; 7]. Video compression methods focused on aerospace applications can also be found [8; 9], aiming at eliminating long-term redundancy among multiple periodically revisited videos. The images acquired by the satellite are compressed and transmitted to the ground-station assuming that: (1) all the images are equally important; and that (2) all the content in each image is equally important too. However, both assumptions are not true for some applications. As mentioned in [10], one of the limitations of small satellite missions is the platform's capacity for using instruments with high power consumption or high data rate requirements. Thus, small satellite missions usually focus on one specific physical phenomenon to be observed and monitored. One can consider as small satellite projects constellations of pico-satellites, nano-satellites, and some of micro-satellites, which are common among constellation projects. In fact, among the satellites constellation projects accounted for in [1], 1% correspond to pico-satellites (mass less than 1 kg), 30% to nano-satellites (mass between 1 and 10 kg), and 18% to micro-satellites (mass between 10 and 100 kg). These percentages could be even greater, since 35% of constellation projects do not give information about the sizes of their satellites. Overall, some acquired images can be considered useless, or partially useless, for some applications. For instance, cloud-covered images may not be useful for applications such as surveillance or deforestation mapping, and as a consequence, there is no need to transmit them to the ground-station. The potential of data saving for such applications is high, since the global cloud coverage (cloud fraction) is approximately 67%, according to information retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) [11]. More precisely, MODIS detected that the cloud fraction over land is around 55%, with a distinctive seasonal cycle, whereas the ocean's cloudiness is around 72%, and it has far less seasonal variation. For oceanic areas, even more images can be considered not useful if the application relies on vessel detection, which fits, for instance, Maritime Domain Awareness (MDA) applications. The potential of data saving for applications where other content than vessels can be discarded is huge, since the oceans cover 70% of the earth's surface. The previous knowledge about which content is not useful allows optimizing the image compression step for further information saving. In that sense, regions of interest (ROI) can be defined by deciding on-the-fly if the content of the images (or part of the content) is actually useful for the target application.