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

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

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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 learn-ing 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 addi-tion 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.

Image segmentationGraph learningBatch active learningHyperspectral image

Bohan Chen、Kevin Miller、Andrea L.Bertozzi、Jon Schwenk

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Department of Mathematics,University of California,Los Angeles,520 Portola Plaza,Los Angeles 90095,CA,USA

Oden Institute for Computational Engineering and Sciences,University of Texas at Austin,201 E 24th St,Austin 78712,TX,USA

Los Alamos National Laboratory,Los Alamos,NM 87545,USA

UC-National Lab In-Residence Graduate Fellowship GrantDOD National Defense Science and Engineering Graduate(NDSEG)Research FellowshipLaboratory Directed Research and Development program of Los Alamos National LaboratoryLaboratory Directed Research and Development program of Los Alamos National LaboratoryNGA&&

L21GF360620170668PRD120210213ERHM04762110003NGA-U-2023-00757

2024

应用数学与计算数学学报
上海大学

应用数学与计算数学学报

影响因子:0.165
ISSN:1006-6330
年,卷(期):2024.6(2)