A Batch-Mode Active Learning Algorithm Using Region-Partitioning Diversity for SVM Classifier

In this paper, a region-partitioning active learning (AL) technique is proposed for classification of remote sensing (RS) images based on the support vector machines (SVM) classifier. In the batch-mode AL process, diversity information is required to select a batch of informative samples. A new AL technique that aims to introduce diversity information is proposed based on relative positions of candidate samples in the feature space. The proposed technique selects informative samples according to an uncertainty criterion at each iteration. These samples are selected with an extra constraint to guarantee that they are not located in the same region of the feature space. The proposed technique is compared with state-of-the-art methods adopted in the RS community. Experimental tests were performed on three data sets, including one very high spatial resolution multispectral data set and two hyperspectral data sets. The proposed algorithm displays a classification performance that is similar to or even better than the state-of-the-art methods. In addition, the proposed algorithm performs efficiently in terms of computational time.

[1]  Mikhail F. Kanevski,et al.  Memory-Based Cluster Sampling for Remote Sensing Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Xiaowei Xu,et al.  Representative Sampling for Text Classification Using Support Vector Machines , 2003, ECIR.

[3]  Melba M. Crawford,et al.  Active Learning: Any Value for Classification of Remotely Sensed Data? , 2013, Proceedings of the IEEE.

[4]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[5]  David J. C. MacKay,et al.  Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.

[6]  Lorenzo Bruzzone,et al.  A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Ishwar K. Sethi,et al.  Confidence-based active learning , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Klaus Brinker,et al.  Incorporating Diversity in Active Learning with Support Vector Machines , 2003, ICML.

[9]  Francesca Bovolo,et al.  Active-learning based cascade classification of multitemporal images for updating land-cover maps , 2011, 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp).

[10]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  Naoki Abe,et al.  Query Learning Strategies Using Boosting and Bagging , 1998, ICML.

[13]  Shlomo Argamon,et al.  Committee-Based Sampling For Training Probabilistic Classi(cid:12)ers , 1995 .

[14]  H. Sebastian Seung,et al.  Query by committee , 1992, COLT '92.

[15]  Lorenzo Bruzzone,et al.  Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Mikhail F. Kanevski,et al.  A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[17]  Sankar K. Pal,et al.  Segmentation of multispectral remote sensing images using active support vector machines , 2004, Pattern Recognit. Lett..

[18]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[19]  Shigeo Abe.  Analysis of Multiclass Support Vector Machines , 2002 .

[20]  William J. Emery,et al.  Active Learning Methods for Remote Sensing Image Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Lawrence O. Hall,et al.  Active learning to recognize multiple types of plankton , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[22]  Lorenzo Bruzzone,et al.  A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  J. A. Gualtieri,et al.  Support vector machines for classification of hyperspectral data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[24]  Melba M. Crawford,et al.  Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[25]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[26]  Joydeep Ghosh,et al.  An Active Learning Approach to Hyperspectral Data Classification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Andrew McCallum,et al.  Toward Optimal Active Learning through Sampling Estimation of Error Reduction , 2001, ICML.

[28]  Melba M. Crawford,et al.  View Generation for Multiview Maximum Disagreement Based Active Learning for Hyperspectral Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Arnold W. M. Smeulders,et al.  Active learning using pre-clustering , 2004, ICML.

[30]  William A. Gale,et al.  A sequential algorithm for training text classifiers , 1994, SIGIR '94.

[31]  Melba M. Crawford,et al.  Critical class oriented active learning for hyperspectral image classification , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[32]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[33]  Nello Cristianini,et al.  Query Learning with Large Margin Classi ersColin , 2000 .

[34]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[35]  P. Strobl,et al.  Pan-European Forest/Non-Forest Mapping with Landsat ETM+ and CORINE Land Cover 2000 Data , 2009 .

[36]  Marin Ferecatu,et al.  Interactive Remote-Sensing Image Retrieval Using Active Relevance Feedback , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Joydeep Ghosh,et al.  Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Prateek Jain,et al.  Far-sighted active learning on a budget for image and video recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[40]  Greg Schohn,et al.  Less is More: Active Learning with Support Vector Machines , 2000, ICML.

[41]  Lorenzo Bruzzone,et al.  A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier , 2012, IEEE Geoscience and Remote Sensing Letters.

[42]  H. Sebastian Seung,et al.  Selective Sampling Using the Query by Committee Algorithm , 1997, Machine Learning.

[43]  Lorenzo Bruzzone,et al.  Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Raymond J. Mooney,et al.  Diverse ensembles for active learning , 2004, ICML.