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Land cover classification of cloud- and snow-contaminated multi-temporal high-resolution satellite images. Arnt-Børre Salberg and Rune Solberg Norwegian Computing Center. 3rd Workshop of the EARSeL SiG Remote Sensing of Land Use and Land Cover, 25 - 27 November 2009, Bonn, Germany. Overview.

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Presentation Transcript
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Land cover classification of cloud- and snow-contaminated multi-temporal high-resolutionsatellite images

Arnt-Børre Salberg and Rune Solberg

Norwegian Computing Center

3rd Workshop of the EARSeL SiG Remote Sensing of Land Use and Land Cover,

25 - 27 November 2009, Bonn, Germany

overview
Overview
  • Motivation & challenges
  • Missing data mechanism
  • Classification with missing observations
  • Image restoration
  • Experiments & Results
  • Summary and Discussions
motivation land cover classification
Motivation –Land cover classification

Multi-spectral image

Thematic map

Classifier

Label

Feature

vector

multi temporal land cover classification
Multi-temporal land cover classification
  • Land cover classification using high-resolution optical remote sensing can be challenging since:
    • In Northern Europe clouds and snow prevent us from observing the surface of the earth.
    • High-resolution images has often a low temporal coverage.
  • Multi-temporal land cover classification
    • Enhanced performance since we observe the vegetation at different phenological states.
    • The set of cloud contaminated images have observed a higher portion of the earth’s surface than a single image.
multi temporal land cover classification by pixel level fusion
Multi-temporal land cover classification by pixel level fusion

Thematic map

Multi-temporal & Multi-spectral images

Classifier

Feature

vector

Label

challenges pixel level fusion
Challenges – Pixel level fusion?

Typical missing data pattern

  • How should we handle the missing observations?
handling missing observations
Handling missing observations

Proposed approach:

  • Identify the missing observations.
  • Identify the missing data mechanism.
  • Construct classifiers capable of handling data with missing features and a given missing data mechanism.
identify missing observations
Identify missing observations
  • Cloud/snow detection
    • Classify the images into the categories: Cloud, snow, water and vegetation/soil/rock.
    • Constructed a missing data indicator ri for each pixel
  • Assume perfect cloud/snow detection
identify the missing data mechanisms
Identify the missing data mechanisms
  • Missing completely at random (MCAR)
    • Landsat 7 sensor failure.
  • Missing at random (MAR)
    • Clouds
  • Not MAR
    • Snow, censoring of measurements
classification with missing observations
Classification with missing observations

Some existing approaches

  • Mean value or zero substitution
    • Biased estimates
  • Remote sensing
    • Aksoy et al. 2009
    • Decision tree based approach
classification with missing observations1
Classification with missing observations

Let x(k) denote the part of x corresponding to the missing data indicator vector rk

Optimal classifier (Mojirsheibani & Montazeri, 2007)

Let r be a binary vector with 0 at the element j if the jth element of x is missing, and 1 otherwise

classification with missing observations2
Classification with missing observations
  • Missing data mechanism introduces an additional probability
    • Depends on feature vector and land cover class.
  • MCAR:
    • Classifier reduces to the marginal distribution where the missing features are integrated out.
parametric classifiers
Parametric classifiers
  • Unknown parameters need be estimated when applying parametric classifiers
  • Only use complete feature vectors for learning
    • May be only a few available
  • Expectation Maximization algorithm often applied for Gaussian distributions or mixture Gaussian distributions
  • Parametric classifiers difficult since

is unknown and hard to estimate.

non parametric classifiers
Non-parametric classifiers
  • K-NN classifier for not MAR scenarios:
    • kNN classifier works on the selection of samples among the training data that has the exact same missing data pattern as the test vector, and perform the kNN rule among these samples
image restoration
Image restoration
  • Assume that a land cover map is available (from the classification module)
  • Minimum mean-squared error estimator (assuming Gaussian distributions)
    • Dependent on the land cover class of the given pixel.
    • mc and Sc estimated using the EM algorithm (MAR assumption)
experiments results
Experiments & Results
  • Land cover classification of mountain vegetation important for biomass estimation of lichen.
    • Remote sensing data: 4 Landsat 7 ETM+ images (2004-05-31, 2000-07-23, 2002-08-14, and 2002-09-15)
    • Ancillary data: Slope and elevation derived from a digital elevation model (DEM).
    • In situ data: 4861 pixels were labeled according to the classes: water, ridge, leeside, snowbed, mire, forest and rock.
results land cover classification
Results– Land cover classification

Input images

Missing data indicators

Thematic map

results cloud removal
Results – Cloud removal
  • Image restoration of July 23 using Aug. 14 and Sep. 15 images.

Input image

Restored image

Cloud shadows

results snow and sensor failure removal
Results – snow and sensor failure removal

Input image

Restored image

  • Image restoration of May 31 image using July 23, Aug. 14 and Sep. 15 images.
  • Note that at May 31 the vegetation is in a different phenological state than for the other images.
summary and discussions
Summary and discussions
  • Proposed a two-stage approach
    • Cloud/snow classification
    • Vegetation type classification with missing observations
  • Obtained increased classification power by pixel level fusion of cloud and snow contaminated satellite images
  • Image restoration natural by product and seem to work good for some areas.
    • Cloud shadows remains a challenge.
    • Difficult for not MAR
summary and discussions1
Summary and discussions
  • Further improvement in classification accuracy expected by
    • Proper feature extraction
    • Contextual classification (e.g. Markov Random Field)
    • Including ancillary data important for mountain vegetation (e.g. bio-climatic variables)
    • Multi-sensor fusion with full polarimetric SAR images?
    • Identification of cloud shadows
    • Topographic illumination correction (c-correction)
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