CONCEPT FOR A COMPOUND ANALYSIS IN ACTIVE LEARNING FOR REMOTE SENSING

Active learning reduces training costs for supervised classification by acquiring ground truth data only for the most useful samples. We present a new concept for the analysis of active learning techniques. Our framework is split into an outer and an inner view to facilitate the assignment of different influences. The main contribution of this paper is a concept of a new compound analysis in the active learning loop. It comprises three sub-analyses: structural, oracle, prediction. They are combined to form a hypothesis of the usefulness for each unlabeled training sample. Though the analysis is in an early stage, different extensions are highlighted. Further we show how variations inside the framework lead to many techniques from the active learning literature. In this work we focus on remote sensing, but the proposed method can be applied to other fields as well.

[1]  Wolfgang Middelmann,et al.  Hyperspectral band selection using statistical models , 2011, Defense + Commercial Sensing.

[2]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[3]  Christopher H. Bryant,et al.  Functional genomic hypothesis generation and experimentation by a robot scientist , 2004, Nature.

[4]  Konrad Schindler,et al.  An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Isabelle Guyon,et al.  Results of the Active Learning Challenge , 2011, Active Learning and Experimental Design @ AISTATS.

[6]  Wolfgang Middelmann,et al.  An approach to fully unsupervised hyperspectral unmixing , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[7]  David A. Cohn,et al.  Improving generalization with active learning , 1994, Machine Learning.

[8]  Nicolas Cebron,et al.  Aktives Lernen zur Klassifikation großer Datenmengen mittels Exploration und Spezialisierung , 2008 .

[9]  Sebastian Wuttke,et al.  Reduction of training costs using active classification in fused hyperspectral and LiDAR data , 2012, Remote Sensing.

[10]  John Langford,et al.  Contextual Bandit Algorithms with Supervised Learning Guarantees , 2010, AISTATS.

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

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

[13]  Andrew McCallum,et al.  Toward Optimal Active Learning through Monte Carlo Estimation of Error Reduction , 2001, ICML 2001.

[14]  Gavin C. Cawley.  Some Baseline Methods for the Active Learning Challenge , 2010 .

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

[16]  Sethuraman Panchanathan,et al.  Adaptive Batch Mode Active Learning , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Vikram Krishnamurthy,et al.  Algorithms for optimal scheduling and management of hidden Markov model sensors , 2002, IEEE Trans. Signal Process..

[18]  José Hernández-Orallo,et al.  Volume under the ROC Surface for Multi-class Problems , 2003, ECML.

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

[20]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

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

[22]  Jie Tang,et al.  Addressing cold start in recommender systems: a semi-supervised co-training algorithm , 2014, SIGIR.

[23]  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.

[24]  Melba M. Crawford,et al.  Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure , 2005, IEEE Transactions on Image Processing.

[25]  David D. Lewis,et al.  Heterogeneous Uncertainty Sampling for Supervised Learning , 1994, ICML.

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

[27]  Devis Tuia,et al.  Learning User's Confidence for Active Learning , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[28]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[29]  Lorenzo Bruzzone,et al.  Definition of Effective Training Sets for Supervised Classification of Remote Sensing Images by a Novel Cost-Sensitive Active Learning Method , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Lorenzo Bruzzone,et al.  Active and Semisupervised Learning for the Classification of Remote Sensing Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  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.

[32]  Dana Angluin,et al.  Queries and concept learning , 1988, Machine Learning.