Ensemble Multiple Kernel Active Learning For Classification of Multisource Remote Sensing Data

Incorporating disparate features from multiple sources can provide valuable diverse information for remote sensing data analysis. However, multisource remote sensing data require large quantities of labeled data to train robust supervised classifiers, which are often difficult and expensive to acquire. A mixture-of-kernel approach can facilitate the construction of an effective formulation for acquiring useful samples via active learning (AL). In this paper, we propose an ensemble multiple kernel active learning (EnsembleMKL-AL) framework that incorporates different types of features extracted from multisensor remote sensing data (hyperspectral imagery and LiDAR data) for robust classification. An ensemble of probabilistic multiple kernel classifiers is embedded into a maximum disagreement-based AL system, which adaptively optimizes the kernel for each source during the AL process. At the end of each learning step, a decision fusion strategy is implemented to make a final decision based on the probabilistic outputs. The proposed framework is tested in a multisource environment, including different types of features extracted from hyperspectral and LiDAR data. The experimental results validate the efficacy of the proposed approach. In addition, we demonstrate that using ensemble classifiers and a large number of disparate but relevant features can further improve the performance of an AL-based classification approach.

[1]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Jon Atli Benediktsson,et al.  Generalized Composite Kernel Framework for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Naif Alajlan,et al.  Optical Image Classification: A Ground-Truth Design Framework , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Jon Atli Benediktsson,et al.  Morphological Attribute Profiles for the Analysis of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Goo Jun,et al.  Spatially Cost-Sensitive Active Learning , 2009, SDM.

[6]  Gustavo Camps-Valls,et al.  Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.

[7]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[8]  Gunnar Rätsch,et al.  Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..

[9]  Hsuan-Tien Lin,et al.  A note on Platt’s probabilistic outputs for support vector machines , 2007, Machine Learning.

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

[11]  William J. Emery,et al.  SVM Active Learning Approach for Image Classification Using Spatial Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Jon Atli Benediktsson,et al.  Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis , 2011, IEEE Geoscience and Remote Sensing Letters.

[13]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Melba M. Crawford,et al.  Extraction of Features From LIDAR Waveform Data for Characterizing Forest Structure , 2012, IEEE Geoscience and Remote Sensing Letters.

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

[16]  Edoardo Pasolli,et al.  A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  N. Cristianini,et al.  On Kernel-Target Alignment , 2001, NIPS.

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

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

[20]  Nello Cristianini,et al.  A statistical framework for genomic data fusion , 2004, Bioinform..

[21]  Ye Zhang,et al.  Representative Multiple Kernel Learning for Classification in Hyperspectral Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[22]  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.

[23]  André Stumpf,et al.  Active Learning in the Spatial Domain for Remote Sensing Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[24]  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.

[25]  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..

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

[27]  Ion Muslea,et al.  Active Learning with Multiple Views , 2009, Encyclopedia of Data Warehousing and Mining.

[28]  Paul Scheunders,et al.  Multisource Classification of Color and Hyperspectral Images Using Color Attribute Profiles and Composite Decision Fusion , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Saurabh Prasad,et al.  Decision Fusion With Confidence-Based Weight Assignment for Hyperspectral Target Recognition , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Yuliya Tarabalka,et al.  Best Merge Region-Growing Segmentation With Integrated Nonadjacent Region Object Aggregation , 2012, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

[34]  Melba M. Crawford,et al.  Multi-view adaptive disagreement based active learning for hyperspectral image classification , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[35]  Gustavo Camps-Valls,et al.  Learning Relevant Image Features With Multiple-Kernel Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Farid Melgani,et al.  Support Vector Machine Active Learning Through Significance Space Construction , 2011, IEEE Geoscience and Remote Sensing Letters.

[37]  Melba M. Crawford,et al.  Multiple kernel active learning for robust geo-spatial image analysis , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.