Arnt-Børre Salberg and Rune Solberg Norwegian Computing Center

1. 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

2. Overview • Motivation & challenges • Missing data mechanism • Classification with missing observations • Image restoration • Experiments & Results • Summary and Discussions

3. Motivation –Land cover classification Multi-spectral image Thematic map Classifier Label Feature vector

4. 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.

5. Multi-temporal land cover classification by pixel level fusion Thematic map Multi-temporal & Multi-spectral images Classifier Feature vector Label

6. Challenges – Pixel level fusion? Typical missing data pattern • How should we handle the missing observations?

7. 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.

8. 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

9. 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

10. Classification with missing observations Some existing approaches • Mean value or zero substitution • Biased estimates • Remote sensing • Aksoy et al. 2009 • Decision tree based approach

11. 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

12. 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.

13. 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.

14. 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

15. Two-stage classifier

16. 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)

17. 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.

18. Results– Land cover classification Input images Missing data indicators Thematic map

19. Results – Cloud removal • Image restoration of July 23 using Aug. 14 and Sep. 15 images. Input image Restored image Cloud shadows

20. 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.

21. Classification results

22. 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

23. 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)

24. Thank you