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Anomalies in Landsat Imagery and Imputation

Authors:

Soumya Goswami,

K. SangeetaAuthors Info & Claims

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Pages 353 - 358

https://doi.org/10.1145/2791405.2791495

Published: 10 August 2015 Publication History

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Abstract

In remote sensing applications the data and imagery are incomplete so that some geographical coordinates will not have measured values of the variables of interest. There are several reasons for the occurrence of image gaps. It can be instrumentation error, losses of image data during transmission or the cloud coverage. These missing values degrade the representativeness of true dataset. This leads to difficulties in several applications due to which recovery of original image form partial or incomplete image is of key importance. In this paper spatial imputation based on machine learning algorithms has been used to fill the unknown pixels in a satellite image. Missing value estimation is better when the imputation algorithm considers the correlation in existing values of the dataset. Types of data loss which we have dealt with in this paper is dropped scans and shutter synchronisation anomalies in satellite image. The variations of k-NN approach have been used to treat the anomalies that occur in land satellite data due to various reasons.

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Cited By

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Willard CWade NStrlič MGilchrist JWeyrich TGibson A(2022)Correction of Dropped Frames in High-resolution Push-broom Hyperspectral Images for Cultural HeritageJournal on Computing and Cultural Heritage 10.1145/347901115:2(1-19)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3479011

Index Terms

Anomalies in Landsat Imagery and Imputation
1. Applied computing
  1. Physical sciences and engineering
    1. Earth and atmospheric sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

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Published In

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

August 2015

763 pages

ISBN:9781450333610

DOI:10.1145/2791405

Editor:
Indu Nair
SCMS, Kochi, India
,
General Chairs:
Sushmita Mitra
Indian Statistical Institute, Kolkata, India
,
Ljiljana Trajković
Simon Fraser University, Canada
,
Program Chairs:
Punam Bedi
University of Delhi, India
,
Suzanne McIntosh
New York University and Cloudera Inc., USA
,
M. S. Rajasree
IIITM-K, Trivandrum, India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

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WCI '15

WCI '15: Third International Symposium on Women in Computing and Informatics

August 10 - 13, 2015

Kochi, India

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WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;

Overall Acceptance Rate 98 of 452 submissions, 22%

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Cited By

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Willard CWade NStrlič MGilchrist JWeyrich TGibson A(2022)Correction of Dropped Frames in High-resolution Push-broom Hyperspectral Images for Cultural HeritageJournal on Computing and Cultural Heritage 10.1145/347901115:2(1-19)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3479011

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An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks