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An adaptive capsule network for hyperspectral remote sensing classification

Ding, Xiaohui; Li, Yong; Yang, Ji; Li, Huapeng; Liu, Lingjia; Liu, Yangxiaoyue; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584. 2021 An adaptive capsule network for hyperspectral remote sensing classification [in special issue: Advances in object-based image analysis - linked with computer vision and machine learning] Remote Sensing, 13 (13), 2445. 17, pp. https://doi.org/10.3390/rs13132445

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Abstract/Summary

The capsule network (Caps) is a novel type of neural network that has great potential for the classification of hyperspectral remote sensing. However, the Caps suffers from the issue of gradient vanishing. To solve this problem, a powered activation regularization based adaptive capsule network (PAR-ACaps) was proposed for hyperspectral remote sensing classification, in which an adaptive routing algorithm without iteration was applied to amplify the gradient, and the powered activation regularization method was used to learn the sparser and more discriminative representation. The classification performance of PAR-ACaps was evaluated using two public hyperspectral remote sensing datasets, i.e., the Pavia University (PU) and Salinas (SA) datasets. The average overall classification accuracy (OA) of PAR-ACaps with shallower architecture was measured and compared with those of the benchmarks, including random forest (RF), support vector machine (SVM), 1-dimensional convolutional neural network (1DCNN), two-dimensional convolutional neural network (CNN), three-dimensional convolutional neural network (3DCNN), Caps, and the original adaptive capsule network (ACaps) with comparable network architectures. The OA of PAR-ACaps for PU and SA datasets was 99.51% and 94.52%, respectively, which was higher than those of benchmarks. Moreover, the classification performance of PAR-ACaps with relatively deeper neural architecture (four and six convolutional layers in the feature extraction stage) was also evaluated to demonstrate the effectiveness of gradient amplification. As shown in the experimental results, the classification performance of PAR-ACaps with relatively deeper neural architecture for PU and SA datasets was also superior to 1DCNN, CNN, 3DCNN, Caps, and ACaps with comparable neural architectures. Additionally, the training time consumed by PAR-ACaps was significantly lower than that of Caps. The proposed PAR-ACaps is, therefore, recommended as an effective alternative for hyperspectral remote sensing classification.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.3390/rs13132445
UKCEH and CEH Sections/Science Areas: Soils and Land Use (Science Area 2017-)
ISSN: 2072-4292
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: capsule network, hyperspectral remote sensing, adaptive routing algorithm, deep learning
NORA Subject Terms: Electronics, Engineering and Technology
Computer Science
Date made live: 13 Jul 2021 14:58 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/530688

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