Speakers
Description
The future Copernicus Hyperspectral Imaging Mission for Environment (CHIME) will provide continuous acquisitions during the daylight part of the orbit, with numerous bands in VNIR and SWIR domains. Considering the significant presence of clouds hiding the ground, a work has been performed in the frame of CHIME A/B1 to explore the possibilities of increasing the on-board data reduction, with a selective compression applied to the clouds. The compression chain includes a Machine Learning-based cloud detection built on Support Vector Machine (SVM) approach that has been selected for its performances and high adaptability for future evolutions. The SVM is defined with appropriate spectral bands and indexes, and the training is performed on-ground, making cloud detection implementable on-board. The output cloud map is then considered by a selective compressor based on CCSDS 123.0-B-2 standard to apply a higher loss on pixels detected as cloud. The design has been coded in VHDL and C language (transformed into VHDL by using High Level Synthesis techniques) and validated in a Xilinx evaluation board, mounting a KU040 FPGA, device representative of flight hardware. The results allow to have an estimation of FPGA resources needs and will be used to select the CHIME flight FPGA.
