Several remote sensing applications that collect specific data in the space environment use images capable of providing a large volume of information, known as hyperspectral images. Given the amount of data, one of the most critical issues in applications that use hyperspectral images is the demanded for compression, which also affects restrictions on the storage capacity and processing in space applications. This work aimed to implement hyperspectral image compressors, considering the standard by the CCSDS (Consultative Committee for Space Data Systems). The solutions were implemented using a High-Level Synthesis tool (HLS) and a manual description in a Hardware Description Language (HDL). Results show that, compared to the software solution, the HLS and the HDL implementation accelerated the application by 1.6× and 4×, respectively. For images up to 512 spectral bands, the HLS solution presented a throughput of 9.11 MSa/s, while the HDL solution can process 21.47 MSa/s, which meets the real-time requirements of the standard. The HDL solution uses about 3× fewer LUTs (Look-Up Tables) LUTs and 8× fewer FFs (Flip-Flops) than the HLS implementation. Due to the low cost observed in the results, we intend to harden the accelerator and integrate it into a future multi-core satellite system.