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

Author:

Chen BohanORCID,Miller Kevin,Bertozzi Andrea L.,Schwenk Jon

Abstract

AbstractGraph 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.

Funder

University of California, Los Angeles

National Defense Science and Engineering Graduate

Los Alamos National Laboratory

National Geospatial-Intelligence Agency

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Applied Mathematics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Graph-Based Active Learning for Surface Water and Sediment Detection in Multispectral Images;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

2. Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing;IEEE Transactions on Geoscience and Remote Sensing;2023

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