Robustness of Lossy Multispectral Compression to Simulated Instrumental Noise: A Comparative Study

Lossy compression of multispectral data has been widely adopted to tackle the downlink bottleneck in space-based Earth observation. However, compression artifacts may interact with the instrumental noise inherent to the sensors, affecting both the performance of the compressors and the effectiveness of denoising techniques. This letter evaluates the effects of lossy compression in the presence of instrumental noise and the corresponding denoising procedures on multispectral data, comparing current techniques for on-board lossy compression (CCSDS 122.0-B-1, near-lossless CCSDS 123.0-B-2, JPEG 2000, and exogenous KLT-JPEG 2000) with recent reduced-complexity models based on neural networks (mijares, chabert, oberlin and serra (MCOS) and spectral orthogonal transform encoder lossY (SORTENY)]. The rate-distortion performance of each codec is analyzed across four simulated instrumental noise conditions: thermal noise, dark signal, missing lines, and parallax-induced band misalignment. Experimental results show that all the evaluated codecs are resilient under thermal noise, dark signal, and missing line artifacts. However, for band misalignment artifacts, the compressors using a linear spectral transform, as E-KLT-JPEG 2000 and SORTENY, experienced a significant degradation.