Collaborative Active Learning Based on Improved Capsule Networks for Hyperspectral Image Classification

计算机科学 人工智能 高光谱成像 水准点(测量) 机器学习 注释 上下文图像分类 人工神经网络 深度学习 模式识别(心理学) 数据挖掘 图像(数学) 大地测量学 地理
作者
Heng Wang,Liguo Wang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-26 被引量:2
标识
DOI:10.1109/tgrs.2023.3309269
摘要

For hyperspectral image classification (HIC) tasks, most uncertainty-based active learning (AL) methods only consider the uncertainty, without considering the diversity of actively selected samples and the budget of expert labeling. In this paper, we propose a collaborative active learning (CAL) framework to address this problem. The proposed framework consists of two well-designed base classifiers and an ingenious CAL scheme that takes into account both the uncertainty and diversity of actively selected samples and the cost of expert annotation. Specifically, get benefit from the capsule networks’ ability to accurately identify and locate features, we design two improved capsule networks. For these two networks, we call the first CapsViT (Capsule Vision Transformer), which introduces Vision Transformer (ViT) into the capsule network (CapsNet) to learn the global relationship between the capsule features. We call the second CapsGLOM (Capsule GLOM), the basic structure of this network is derived from the GLOM system proposed by Geoffrey Hinton, we learn from the way CapsNet constructs the primary capsules to improve its implementation details. CapsViT and CapsGLOM are used as the two base classifiers in the proposed CAL framework to select the most informative samples according to the CAL scheme under the premise of fully considering the cost of expert annotation. Experimental results on four benchmark hyperspectral image data sets show that our proposed CAL framework can achieve satisfactory classification results. At the same time, compared with other advanced deep models, our proposed CapsViT and CapsGLOM are also competitive in the supervised HIC tasks. The source code can be available online (https://github.com/swiftest/CAL).
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