In recent years, hyperspectral sensors for remote sensing of the Earth have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data must be reduced on-board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature have focused the attention to efficient way of on-board data compression, since this is a challenge task due to the difficult environment (outer space), and due to the limited power and computing resources. The current work proposes a framework for on-board operations such as: automatic recognition of target types or detection of events in near real time, in regions of interest with an unsupervised classifier; the compression of specific regions with different bit rates compared to the remaining acquisition (background); the management of the data volume to be transmitted to the Ground Station. Experiments are shown using real data taken from AVIRIS airborne sensor in a harbor area.
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