e-mail: claudio.persello@disi.unitn.it, lorenzo.bruzzone@ing.unitn.it,

1. A NOVEL ACTIVE LEARNING STRATEGY FOR DOMAIN ADAPTATION IN THE CLASSIFICATION OF REMOTE SENSING IMAGES C. Persello L. Bruzzone e-mail: claudio.persello@disi.unitn.it, lorenzo.bruzzone@ing.unitn.it, Web page: http://rslab.disi.unitn.it

2. Outline Background on Domain Adaptation and Active Learning 1 Aim of the Work 2 Proposed Approach to Address Domain Adaptation Problems with Active Learning 3 Experimental Results 4 Conclusions 5 C. Persello, L. Bruzzone

3. Introduction Scenario: Growing availability of space-borne datathat gives the opportunity to develop several applications related to land-cover mapping andmonitoring. Problem: Common automatic classification techniques are based on supervised learning methods, which require a set of new training samples every time that a new remote sensing image has to be classified Need for the developmentof efficient techniques capable to adapt the supervised classifier trained on a image for the classification of another similar but not identical image acquired either: 1) on a different area,or 2) on the same area at a different time. C. Persello, L. Bruzzone

4. Background on Domain Adaptation Domain Adaptation: models the problem of adapting a supervised classifier trained on a given image (source domain) to the classification of another similar but not identical image (target domain) acquired either on a different area, or on the same area at a different time. Assumption: Source and target domainshare the same set of land cover classes. Source Domain Target Domain Semisupervisedtechniques (e.g., [1], [2]) Problem: correct converngence is not always possible Class Class Unknown Class Class • [1] L. Bruzzone, D. Fernandez Prieto, “Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote-sensing images,” IEEE Trans. Geosci. Remote Sens., Vol. 39, No.2, pp. 456-460, 2001. • [2] L. Bruzzone, M. Marconcini, “Domain Adaptation Problems: a DASVM Classification Technique and a Circular Validation Strategy,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 32, 2010, No. 5, pp. 770-787, 2010. C. Persello, L. Bruzzone

5. Working Assumption Working Assumption: In this work we assume that some samples (as little as possible) from the target domain can be labeled by the user and added to the existing training set. Proposed solution: use of Active Learning [1], [2] procedure for selecting the most informative samples of the target domain. General Active Process U G: Supervisedclassifier; Q: Query function; S: Supervisor; T: Training set; U: Unlabeleddata classification Ti Ti-1 G Update T X S Q [1] S. Rajan, J. Ghosh, and M. M. Crawford, “An active learning approach to hyperspectral data classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1231-1242, Apr. 2008. [2]B. Demir, C. Persello, and L. Bruzzone, “Batch mode active learning methods for the interactive classification of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no.3, pp. 1014-1031, March 2011. C. Persello, L. Bruzzone

6. Aim of the Work Aim of the Work: propose a novel Domain Adaptation technique based on Active Learning, which aims at classifying the target image, while requiring the minimum number of labeled samples from the new image. Basic Idea: iterative process based on labeling and adding to the training set the most informative samples from the target domain (query+), while removingfrom the training set the source-domain samples that do not fit with the distributions of the classes in the target domain (query-). Example: Source Domain Target Domain Query- Query+ Convergence reached! Class Class Class C. Persello, L. Bruzzone

7. Proposed Technique Classification technique: Gaussian Maximum Likelihood , Query+: selects the batch of the most informative samples from the pool of unlabeled samples, which are taken from the target domain. Second largest class-conditional density Largest class-conditional density x C. Persello, L. Bruzzone

8. Proposed Technique Query-: removesfrom the source-domain training set the labeled samples that do not fit withthe distribution of the classes in the target domain. Class-conditional density computed using source-domain samples Class-conditional density computed using samples at iteration i x - x C. Persello, L. Bruzzone

9. Proposed Technique Combination of Query+ and Query-: Both queries work at the same time on the basis of the following parameters: • number of samples selected by q+; • number of samples selected by q-; Stop Criterion: we considered the Bhattacharyya distance: The active learning process is stopped when reaches a stable saturation point. This allows the user to detect the convergence of the algorithm without a test set on the target domain Class-conditional density computed using source-domain samples Class-conditional density computed using samples at iteration i C. Persello, L. Bruzzone

10. Data Set Description: VHR data set Data set: Two Quickbirdimages acquired in 2006 over two rural areas in Trento, Italy. Reference labeled data: Two sets of labeled samples for each image. Land-cover classes: Vineyard, water, agriculture fields, forest, apple tree, urban area. Image QB1 Image QB2 C. Persello, L. Bruzzone

11. Data Set Description Distribution of labeled samples on bands 3 and 4 of the two Quickbird images Source Domain Target Domain C. Persello, L. Bruzzone

12. Experimental Results Averaged learning curves over ten trials • 10 initial training sets optimized for the classification of QB1 • Initial training set size: 965 samples • For the proposed technique we used: C. Persello, L. Bruzzone

13. Data Set Description: hyperspectraldata set • Study area: Okavango Delta,Botswana. • Data set: Hyperspectral image acquired by the Hyperion sensor of the EO-1 satellite (145 noise free bands). • Classes: 14 different land-cover types. • Reference labeled data was collected in two disjoint areas and four different sets were defined: • a training set T1 • a spatially correlated test set TS1 • a training set T2 spatially disjoint from T1 • a test set TS2 spatially correlated with T2 Area 1 T1 TS1 T2 TS2 Area 2 C. Persello, L. Bruzzone

14. Experimental Results Averaged learning curves over ten trials • 10 initial training sets optimized for the classification of Area 1 • Initial training set size: 707 samples • For the proposed technique we used: C. Persello, L. Bruzzone

15. Conclusion • A novel approach to address Domain Adaptation problems with Active Learning has been proposed. • Assuming that an image and the related reference labeled samples are available, the proposed technique can be used either: • to classify another image acquired on another geographical area with similar characteristics and the same land-cover classes, or • 2) to update the land-cover map given a new image acquired on the same area at a different time. • We introduced a stop criterionthat does not requirea test set defined on the target domain. • Future Developments: • Include a diversity criterion in the query+ function. • Extend the proposed method to kernel-based classifiers. C. Persello, L. Bruzzone