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Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks.

Michalis Giannopoulos1,2, Anastasia Aidini1,2, Anastasia Pentari1,2

  • 1Signal Processing Lab (SPL), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Crete, Greece.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

Lossy compression of multispectral satellite data creates artifacts that harm land-cover classification. A new tensor completion method efficiently recovers data, preserving classification accuracy even with missing observations.

Keywords:
compressionconvolutional neural networksdeep learningmultispectral image classificationnuclear normquantizationresidual learningtensor unfoldings

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Area of Science:

  • Earth observation
  • Remote sensing
  • Data compression

Background:

  • Multispectral sensors generate high-dimensional Earth observation data, requiring compression for storage and transmission.
  • Lossy compression introduces artifacts that can degrade the utility of remote sensing data, particularly for downstream machine learning tasks.
  • Existing compression methods struggle to preserve the integrity of complex, multi-dimensional data structures.

Purpose of the Study:

  • To develop a resource-efficient compression scheme for multispectral Earth observation data.
  • To investigate the impact of data compression on deep learning-based land-cover classification.
  • To propose a novel method for mitigating compression-induced artifacts and handling missing data.

Main Methods:

  • Encoding multispectral observations into high-order tensor structures.
  • Applying quantized low-rank tensor completion for data compression and recovery.
  • Evaluating compression performance using image quality metrics and land-cover classification accuracy.
  • Utilizing ESA Sentinel-2 satellite data for experimental analysis.

Main Results:

  • Minimal compression significantly degrades the performance of deep learning land-cover classification models.
  • The proposed quantized low-rank tensor completion scheme effectively reduces compression artifacts.
  • The recovery scheme demonstrates robustness in addressing missing observations caused by cloud cover.
  • The method preserves classification accuracy even under significant compression ratios.

Conclusions:

  • Data compression is essential for Earth observation but poses challenges for machine learning applications.
  • Quantized low-rank tensor completion offers an effective solution for compressing multispectral data while preserving its analytical value.
  • The proposed method enhances the reliability of remote sensing data for critical applications like land-cover mapping.