Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 45Remote-Sensing Image ClassificationSatellite Image Processing and PhotogrammetryImage and Signal Denoising Methods

The DOI matches its Crossref record.

Machine Learning based Image Processing Techniques for Satellite Image Analysis -A Survey

Asokan, Anju, Anitha, J·2019·2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon)
DOI 10.1109/comitcon.2019.8862452
Open this paper in CoChat — free account

Abstract

This paper presents the detailed comparison of various image processing techniques for analyzing satellite images. The satellite images are large in size, acquired from long distances and are affected by noise and other environmental conditions. Hence it is necessary to process them so that they can be used by the researchers for analysis. Satellite images are widely used in many real time applications such as in agriculture land detection, navigation and in geographical information systems. In this paper, a review of some popular machine learning based image processing techniques is presented. Also a detailed comparison of various techniques is performed. Limitations in each image processing method are also described. In addition to reviewing of different methods, different metrics for performance evaluation in each of the image processing areas is studied.

Overview

Integrity signals

Registry

Crossref · 10.1109/comitcon.2019.8862452

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 25

  1. zhang, 2017, Saliency detection and region of interest extraction based on multi-image common saliency analysis in satellite images, Neurocomputing, 1
  2. 10.1016/j.isprsjprs.2017.11.019 · DOI
  3. 10.1016/j.isprsjprs.2017.11.017 · DOI
  4. 10.1016/j.isprsjprs.2017.01.012 · DOI · Paper page
  5. 10.1016/j.isprsjprs.2016.10.010 · DOI
  6. 10.1016/j.isprsjprs.2017.11.009 · DOI
  7. 10.1016/j.aeue.2015.11.004 · DOI
  8. 10.1016/j.ins.2017.09.010 · DOI
  9. 10.1016/j.asoc.2016.10.012 · DOI
  10. 10.1016/j.ins.2016.02.021 · DOI
  11. 10.1016/j.isprsjprs.2017.04.015 · DOI
  12. singh, 2017, A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement, Computers and Electrical Engg, 1
  13. 10.1016/j.eswa.2016.03.032 · DOI
  14. 10.1016/j.sysarc.2015.11.006 · DOI
  15. 10.1016/j.isprsjprs.2017.11.009 · DOI
  16. 10.1016/j.isprsjprs.2016.10.010 · DOI
  17. 10.1016/j.asoc.2017.08.019 · DOI · Paper page
  18. pare, 2017, A new technique for multilevel color image thresholding based on modified fuzzy entropy and Levy flight firefly algorithm, Computers and Electrical Engg, 1
  19. chang, 2017, A flexible contrast enhancement method with visual effects and brightness preservation: Histogram planting, Computers and Electrical Engg, 1
  20. 10.1016/j.patcog.2017.06.036 · DOI
  21. 10.1016/j.jvcir.2016.11.017 · DOI
  22. 10.1016/j.renene.2016.05.066 · DOI
  23. 10.1109/TGRS.2017.2743243 · DOI
  24. 10.1016/j.isprsjprs.2017.06.001 · DOI
  25. 10.1016/j.cageo.2016.12.013 · DOI

Cited by

Metadata

Authors
Asokan, A., & Anitha, J.
Affiliations
Karunya University (IN)
ORCID
Venue
2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon)
Publisher
IEEE
Published
2019-02
Pages
119-124
DOI
10.1109/comitcon.2019.8862452
OpenAlex
W2979785627
Cited by
45
References
0
Citations by year
2026: 1 · 2025: 6 · 2024: 8 · 2023: 5 · 2022: 10 · 2021: 8 · 2020: 7
FWCI
10.03
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords
satellite image analysis, machine learning image processing, image denoising, image segmentation, performance indicators, remote sensing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review