earthobs.nr.no 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, November 2009, Bonn, Germany

earthobs.nr.no Overview ► Motivation & challenges ► Missing data mechanism ► Classification with missing observations ► Image restoration ► Experiments & Results ► Summary and Discussions

earthobs.nr.no Motivation – Land cover classification Classifier Multi-spectral imageThematic map Feature vector Label

earthobs.nr.no 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.

earthobs.nr.no Multi-temporal land cover classification by pixel level fusion Multi-temporal & Multi-spectral images Thematic map Feature vector Label Classifier

earthobs.nr.no Challenges – Pixel level fusion? Image 1XXXX Image 2XXXXX Image 3XXXXXX Pixel no Typical missing data pattern ► How should we handle the missing observations?

earthobs.nr.no 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.

earthobs.nr.no Identify missing observations ► Cloud/snow detection ▪Classify the images into the categories: Cloud, snow, water and vegetation/soil/rock. ▪Constructed a missing data indicator r i for each pixel ► Assume perfect cloud/snow detection

earthobs.nr.no 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

earthobs.nr.no Classification with missing observations Some existing approaches ► Mean value or zero substitution ▪Biased estimates ► Remote sensing ▪Aksoy et al ▪Decision tree based approach

earthobs.nr.no Classification with missing observations Let x (k) denote the part of x corresponding to the missing data indicator vector r k Optimal classifier (Mojirsheibani & Montazeri, 2007) Let  be a binary vector with 0 at the element j if the jth element of x is missing, and 1 otherwise

earthobs.nr.no 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.

earthobs.nr.no ► 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. Parametric classifiers

earthobs.nr.no ► 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 Non-parametric classifiers

earthobs.nr.no Two-stage classifier

earthobs.nr.no ► 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.  c and  c estimated using the EM algorithm (MAR assumption) Image restoration

earthobs.nr.no Experiments & Results ► Land cover classification of mountain vegetation important for biomass estimation of lichen. ▪Remote sensing data: 4 Landsat 7 ETM+ images ( , , , and ) ▪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.

earthobs.nr.no Results– Land cover classification Input imagesMissing data indicatorsThematic map

earthobs.nr.no Results – Cloud removal Input imageRestored image Cloud shadows ► Image restoration of July 23 using Aug. 14 and Sep. 15 images.

earthobs.nr.no Results – snow and sensor failure removal Input imageRestored 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.

earthobs.nr.no Classification results MethodJuly Aug Sep DEMAcc. excl. missing data Acc. incl. missing data Portion classified Gauss.X69%52%63% X 57%76% X67%66%99% XXXX78% 100% K-NNX68%51%63% X 57%76% X67%66%99% XXXX81% 100%

earthobs.nr.no 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

earthobs.nr.no 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)

earthobs.nr.no Thank you