Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

doi:10.1007/s42967-023-00284-8

Communications on Applied Mathematics and Computation ›› 2024, Vol. 6 ›› Issue (2): 1013-1033.doi: 10.1007/s42967-023-00284-8

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Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Bohan Chen¹, Kevin Miller², Andrea L. Bertozzi¹, Jon Schwenk³

1. Department of Mathematics, University of California, Los Angeles, 520 Portola Plaza, Los Angeles 90095, CA, USA;
2. Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin 78712, TX, USA;
3. Los Alamos National Laboratory, Los Alamos, NM 87545, USA

Received:2022-11-27 Revised:2023-04-11 Accepted:2023-05-04 Online:2023-07-20 Published:2023-07-20
Contact: Bohan Chen,E-mail:bhchenyz@g.ucla.edu;Kevin Miller,E-mail:ksmiller@utexas.edu;Andrea L. Bertozzi,E-mail:bertozzi@math.ucla.edu;Jon Schwenk,E-mail:jschwenk@lanl.gov E-mail:bhchenyz@g.ucla.edu;ksmiller@utexas.edu;bertozzi@math.ucla.edu;jschwenk@lanl.gov
Supported by:
Bohan Chen is supported by the UC-National Lab In-Residence Graduate Fellowship Grant L21GF3606. Kevin Miller was supported by a DOD National Defense Science and Engineering Graduate (NDSEG) Research Fellowship. Jon Schwenk is supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project numbers 20170668PRD1 and 20210213ER. Andrea Bertozzi is supported by the NGA under Contract No. HM04762110003. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NGA. Approved for public release, NGA-U-2023-00757.

Abstract

Abstract: Graph learning, when used as a semi-supervised learning (SSL) method, performs well for classification tasks with a low label rate. We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi- or hyperspectral image segmentation. Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification. This work builds on recent advances in the design of novel active learning acquisition functions (e.g., the Model Change approach in arXiv:2110.07739) while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods. In addition to improvements in the accuracy, our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.

Key words: Image segmentation, Graph learning, Batch active learning, Hyperspectral image

Bohan Chen, Kevin Miller, Andrea L. Bertozzi, Jon Schwenk. Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs[J]. Communications on Applied Mathematics and Computation, 2024, 6(2): 1013-1033.

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References

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Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

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