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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 5, pp. 63-72

Study of the impact of lossy data compression using the LERC algorithm on the applicability of remote sensing data for monitoring tasks

A.A. Proshin 1 , E.A. Loupian 1 , M.A. Burtsev 1 , K.A. Troshko 1 , A.V. Kashnitskii 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 23.10.2025
DOI: 10.21046/2070-7401-2025-22-5-63-72
The paper examines the impact of compressing satellite remote sensing data with quality loss using the LERC algorithm on the quality and applicability in real-world tasks of the products obtained on their basis. To assess this impact, the maximum permissible error that can be introduced during compression was evaluated, a test data sample, representing a year-long time series, was created and compressed using the LERC algorithm, then a series of comparisons was conducted. The original and lossy compressed series were compared in terms of NDVI (Normalized Difference Vegetation Index) values for individual scenes, the quality of the daily time series restoration, and the NDVI indices obtained from the restored series were assessed. Also, the quality of solution of object classification task using the Random Forest classifier on the constructed time series was evaluated. All experiments showed almost complete agreement of the calculation results with an error not exceeding the accuracy of the data themselves. The results demonstrate that, provided the criteria for preserving data quality are met, the use of LERC, enabling controlled introduction of errors into the data, provides processing and classification results comparable to those obtained from the original data with approximately a 2.5-fold decrease in the volume of stored data.
Keywords: remote sensing, data compression, LERC, lossy compression, Random Forest
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