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Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
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Managed by UT-Battellefor the Department of Energy
Recent Advances in Crop Classification
Raju Vatsavai([email protected])
Computational Sciences and Engineering Division
ORNL, Oak Ridge, TN, USA
Collaborators:
B. Bhaduri, V. Chandola, G. Jun, J. Ghosh, S. Shekhar, T. Burk
Remote Sensing – Beyond Images Workshop, Mexico City, Mexico,
14th December, 2013.
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Outline
· Better spectral and spatial resolution– Fine-grained (species) classification– Complex (compound) object recognition
· Challenges– Limited ground-truth: Semi-supervised learning (SSL)– Spatial homogeneity: SSL + Markov Random Fields– Spatial heterogeneity: Gaussian Process (GP) learning– Aggregate vs. Subclasses: Fine-grained classification– Phenology: Multi-view learning
· Conclusions
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Challenge 1: Limited Training Data
· Increasing spectral resolution: 4 to 224 Bands
· Challenges– #of training samples ~ (10 to 30) * (number of dimensions)– Costly ~ $500-$800 per plot (depends on geographic
area)– Accessibility – Private/Privacy issues (e.g., USFS may
average 5% denied access)– Real-time – Emergency situations, such as, forest fires,
floods
· Solutions– Reduce number of dimensions– (Artificially) Increase number of samples– By incorporating unlabeled samples
· Naïve semi-supervised (Nigam et al. [JML-2000])– Bagging [Breiman, ML-96]
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True Distribution
Estimated Distribution(Small Samples; MLE are good asymptotically)
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Initial Estimates +Unlabeled Samples
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Iteratively Update Parameters Using Unlabeled Samples
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Iteratively Update Parameters Using Unlabeled Samples
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Iteratively Update Parameters Using Unlabeled Samples
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Final parameters after convergence
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· E-Step
· M-Step
ithdata vector, jth class
Solution: Semi-supervised Learning
Assume Samples are generated by a Gaussian Mixture Model (GMM)
• Estimate Parameters with Expectation Maximization (EM)
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Results
10 Classes, 100 Training Samples(10-30) x No of dimensions / class
Small Subset of 20 Training Samples
20 labeled + 80 unlabeled samples
Supervised (BC) vs. Semi-supervised (BC-EM)
Fixed Unlabeled (85) and Varying (Increasing) Labeled
0 20 40 60 80 100 120
Acc
urac
y
30
40
50
60
70
80
BC - WorstBC - BestBC (EM) - Best
Ranga Raju Vatsavai, Shashi Shekhar, Thomas E. Burk: A Semi-Supervised Learning Method for Remote Sensing Data Mining. ICTAI 2005: 207-211
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Prior Distribution Model:· For Markov random field , the conditional
distribution of a point in the field given all other points is only dependent on its neighbors.
s excluding Sin points ofset a denotes
lattice imagean is Where
)}(|)({)}(|)({
sS
S
sspsSsp
sxx
xx
sxx
xxx x
x xsxx
xxx
x x
xx
x
xx
Challenge 2: Spatial Homogeneity
Bayes Theorem: p(c|x) = p(x|c)p(c)/p(x)
For a first - order neighborhood system
p() 1
ze
t c ( )C
e.q.1
t c () is the total number of horizantally
and vertially neighboring points of different
value in in clique c.
e.q.1 is Gibbs distribution and therefore,
an MRF.
is emphirically determined weight.
t c () {0, otherwise. 1 if (i, j ) (k,l )
Spatial Homogeneity
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Solution: Spatial Classification
BC (60%) BC-EM (68%)
BC-MRF (65%) BC-EM-MRF (72%)
• Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsavai, Weili Wu, Sanjay Chawla: Spatial contextual classification and prediction models for mining geospatial data. IEEE Transactions on Multimedia 4(2): 174-188 (2002)
• Baris M. Kazar, Shashi Shekhar, David J. Lilja, Ranga Raju Vatsavai, R. Kelley Pace: Comparing Exact and Approximate Spatial Auto-regression Model Solutions for Spatial Data Analysis. GIScience 2004: 140-161
• Ranga Raju Vatsavai, Shashi Shekhar, Thomas E. Burk: An efficient spatial semi-supervised learning algorithm. IJPEDS 22(6): 427-437 (2007)
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Challenge 3: Spatial Heterogeneity· Going From Local to Global
– Signature continuity is a problem in classifying large geographic regions
· Solutions– Assume constant variance structure over space, that is,
train one model, use it on other regions – poor performance
– Train separate model for each region – needs lot of data– Train one model covering samples from all regions –
needs an adaptive model to capture spatial heterogeneity
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Solution: Gaussian Process (GP) Classification
· Change of distribution over space is modeled by),(~)|( Nyxp
))(),((~)|)(( ssNysxp
Goo Jun, Ranga Raju Vatsavai, Joydeep Ghosh: Spatially Adaptive Classification and Active Learning of Multispectral Data with Gaussian Processes. SSTDM 2009: 597-603
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Challenge 4: Aggregate Vs. Sub-classes
· Spectral Classes vs. Thematic Classes
· Insufficient Ground-truth· Subjective/domain-dependent· Parametric – assumption violations
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Solution: Sub-class Classification
· Coarse-to-fine Resolution Information Extraction– Characterizing the nature of the change
· Fallow to Switch grass, Wheat to Corn, or crop damage
Coarse Classes (MODIS)Each class is Gaussian
Sub-Classes (AWiFS)Each class is MoG
Model Selection (BIC,AIC)How many components?Parameter Estimation
Semi-supervised Learning
Characterize Changes
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Results: Sub-class Classification
Dataset:LandSat ETM+ Data (Cloquet, Carleton, MN, May 31, 2000)• 6 Bands, 4 Classes, 60 plots• Independent test data: 205 plots• Forest (4 Subclasses; 2 subclasses are
combined into 1)• 2 Labeled plots per sub-class
1. Ranga Raju Vatsavai, Shashi Shekhar, Budhendra L. Bhaduri: A Learning Scheme for Recognizing Sub-classes from Model Trained on Aggregate Classes. SSPR/SPR 2008: 967-976
2. Ranga Raju Vatsavai, Shashi Shekhar, Budhendra L. Bhaduri: A Semi-supervised Learning Algorithm for Recognizing Sub-classes. SSTDM 2008: 458-467
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Crop (Opium) Classification
· Helmand accounts for 75% of the world’s opium production
· GeoEye 4-Band Image, 13th May 2011
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Ground-truth (Aggregate Classes)
· Ground-truth collected for 4 classes
· 1-Other Crops (Yellow), 2-Poppy (Red), 3-Soils (Cyan), 4-Water (Blue)
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Classified (Aggregate) Image
· Maximum Likelihood Classification (Widely used)
· Also did lot of other standard classification schemes – Decision Trees, Random Forest, Neural Nets, …
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Classified (Sub-classes) Image
· Sub-class classification – Identifying finer classes from aggregate class – new scheme– 1 -> 11,12,13; 2 -> 21,22,23, 3->31,32, 4->41
· (Overall Accuracy Improved by ~10%)
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Challenge 5: Phenology
AWiFS (May 3, 2008; FCC (4,3,2))
AWiFS (July 14, 2008; FCC (4,3,2))
Thematic Classes: C-Corn, S-Soy
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More Formally
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Solution: Multi-view Learning
· Multi-temporal images are different views of same phenomena– Learn single classifier on different views, chose
the best one through empirical evaluation– Combine different views into a single view, train
classifier on single combined view – stacked vector approach
– Learn classifier on single view and combine predictions of individual classifiers – multiple classifier systems
· Bayesian Model Averaging
– Co-training· Learn a classifier independently on each view· Use predictions of each classifier on unlabeled
data instances to augment training dataset for other classifier
Varun Chandola, Ranga Raju Vatsavai: Multi-temporal remote sensing image classification - A multi-view approach. CIDU 2010: 258-270
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Conclusions· We developed several innovative solutions that
address big spatiotemporal data challenges– Semi-supervised learning– Spatial classification (homogeneity and heterogeneity)– Temporal classification– Sub-class classification
· Ongoing– Transfer learning: Adopt model learned in area to the
other with very little additional ground-truth– Compound object classification (multiple instance
learning)– Semantic classification (beyond pixels and objects)– Scaling
· Heterogeneous (OpenMP + MPI + CUDA)· Cloud computing (MapReduce)
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Acknowledgements
· Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR22725.
· Collaborators and Sponsors