Competitive Segmentation Performance on Near-Lossless and Lossy Compressed Remote Sensing Images

Joaquín García-Sobrino*, Armando J. Pinho, Joan Serra-Sagristà

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it affects later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from several instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios (CRs). In some scenarios, accurate segmentation performance can be achieved even for high CRs.

Original languageAmerican English
Article number8820148
Pages (from-to)834-838
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number5
Publication statusPublished - 1 May 2020


  • Image segmentation
  • JPEG 2000
  • lossy compression
  • maximum likelihood (ML)
  • near-lossless compression
  • remote sensing data
  • successive band merging (SBM)


Dive into the research topics of 'Competitive Segmentation Performance on Near-Lossless and Lossy Compressed Remote Sensing Images'. Together they form a unique fingerprint.

Cite this