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Critical Class Oriented Active Learning for Hyperspectral Image Classification

Critical Class Oriented Active Learning for Hyperspectral Image Classification. Wei Di and Melba Crawford. School of Civil Engineering, Purdue University and Laboratory for Applications of Remote Sensing Email: {wdi@purdue.edu 1 , mcrawford 2 }@purdue.edu July 28, 2011

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Critical Class Oriented Active Learning for Hyperspectral Image Classification

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  1. Critical Class Oriented Active Learning for Hyperspectral Image Classification Wei Di and Melba Crawford School of Civil Engineering, Purdue University and Laboratory for Applications of Remote Sensing Email: {wdi@purdue.edu1, mcrawford2}@purdue.edu July 28, 2011 IEEE International Geoscience and Remote Sensing Symposium

  2. Outline • Background Critical Class Oriented Active Learning(AL) • Proposed Methods (SVM-CC, SVM-CCMS) • Guided & ActiveLearning • Critical Class Oriented • Margin Sampling Based • Experimental Results • Conclusions & Future Work

  3. I. BACKGROUND

  4. Motivation Sampling Strategy DL Pool Intelligent sampling strategy Training Data Supervised Classifier • Achieve better performance • Higher utility, low redundancy • Economically allocate resources for labeling • Focus on a specific task or requirement Target H

  5. Active Learning Active Learning (AL) - Iterative learning circle Passive Learning Supervised Classifier Query Strategy DL Pool New xL DU Pool Output Classifier Training xU f(xu) Uncertainty & Critical Class

  6. Introduction • Active Learning in remote sensing • Classification: Tuia et al. [2009], Patraand Bruzzone [2011] Demiret al. [2011], Di and Crawford [2011], . • Segmentation: Jun et al. [2010] • Critical Class oriented Active Learning - Shifting hyperplaneby pair-wise SVM • Identify “Difficult” Classes • Category based query & margin sampling • Goal Provide concept level guidance for building training set favoring “difficult” classes

  7. II. PROPOSED METHOD

  8. Key Idea: Shifting Hyperplane Pair-wise Class A and B Changing Hyperplane Hyperplane w Hyperplane Margin Margin Support Vectors Class A Class B New Samples

  9. Critical Class Identification • Query-based Regularizer wk - hyperplane vector by SVM for kth binary class at the t thquery. • Accumulated Margin Instability Measure the cumulative changes • Order Statistic  Rank class pairs:  Prob. of the kth class pair at critical level CL :

  10. Critical Class Query • Query • SVM-CC • Random Query From Critical Class Set • SVM-CCMS • Query Sample within Critical Class set and closest to margin Critical Class Set Critical Class Identification • Higherprobability • Critical Class Pair • Critical Class Set

  11. III. EXPERIMENTAL RESULTS

  12. Data Description • Kennedy Space Center & Botswana Data • AVIRIS hyperspectral data • Acquired on March, 1996 • 176 of total 224 bands • Spectral bandwidth 10nm • Spatial resolution 18m * Denotes the hard classes

  13. Experimental Results 18 26 18 10th 30th AMI as learning process KSC BOT 18 26 18 • Accumulated Margin Instability (AMI)

  14. Experimental Results DT • Learning Curve • Per-Class Improvement vs RS DU

  15. Experimental Results Per-Class Sampling Ratio KSC • Per-class Sampling Ratio • Ratio of Support Vectors CCMS SVMMS CC RS

  16. IV. CONCLUSIONS AND FUTURE WORK

  17. Conclusions & Future Work • Conclusions • Shifting Hyperplane – Provides valuable information for identifying difficult classes. • Critical Class Oriented Margin Sampling– Focuses on difficult classes, as well as informative samples, improve performance in multi-class problem. • Support Vectors - Concentrate on samples likely to be support vectors. • Future work • Investigation of feature subspaces for identifying the critical classes. • Design proper sample-wise utility score to enhance the category based query.

  18. IV. CONCLUSIONS AND FUTURE WORK

  19. Conclusions & Future Work • Conclusions • Shifting Hyperplane – Provides valuable information for identifying difficult classes. • Critical Class Oriented Margin Sampling– Focuses on difficult classes, as well as informative samples; improves performance in multi-class problem. • Support Vectors - Concentrate on samples likely to be support vectors. • Future work • Investigation of the feature subspace for identifying the critical classes. • Design proper sample-wise utility score to enhance the category based query.

  20. Thanks very much!

  21. Critical Class Identification Process • Accumulative Margin Instability • Critical Class Probability Heat Map

  22. Experimental Results (a) KSC: RS (b) KSC: SVMMS Per-class Learning Performance (c) KSC: SVM-CC (d) KSC: SVM-CCMS

  23. Experimental Results RS SVMMS • BOT • Ratio of Support Vectors SVM-CCSVM-CCMS

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