Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs
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.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 1991631
- Alternate ID(s):
- OSTI ID: 2500896
- Journal Information:
- Communications on Applied Mathematics and Computation, Journal Name: Communications on Applied Mathematics and Computation Journal Issue: 2 Vol. 6; ISSN 2096-6385
- Publisher:
- Springer Science + Business MediaCopyright Statement
- Country of Publication:
- China
- Language:
- English
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