Recent Advances in Crop Classification
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Transcript of Recent Advances in Crop Classification
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