Hierarchical capsule network for hyperspectral image classification

Shi, Meilin; Wang, Ruoxiang; Ren, Jiansi

doi:10.1007/s00521-023-08664-0

Cited by 2 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to its advantages, the capsule network has been applied in image recognition [26] and fault classification [27]. Paoletti et al constructed a deep convolutional capsule network to classify hyperspectral images, showing a higher classification accuracy [28].…”

Section: Capsule Network and Related Applicationsmentioning

confidence: 99%

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Huang,

Zhang,

Shao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Surface roughness measurement is of great importance for monitoring machining quality. To address the performance degradation of deep models caused by low illumination and ambient noise, this study proposes a non-contact surface roughness detection method based on an adaptive deep fusion capsule network (ADFCNet) model. Firstly, principal competent analysis-based color image enhancement is employed to augment initial surface images collected from normal illumination. Then, consisting of a deep multi-model fusion for high-level feature representation and a capsule classifier for roughness recognition, the ADFCNet model is designed to detect roughness grades by using workpiece surface images. The key hyperparameters of the model are automatically determined by a genetic algorithm. Finally, laboratory and factory experiments under low illumination are carried out to validate the effectiveness and superiority of the proposed method. Experimental results demonstrate that the proposed method has strong low-illumination and noise robustness and generalization capability, indicating a better application prospect in actual scenarios.

show abstract

Section: Capsule Network and Related Applicationsmentioning

confidence: 99%

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Huang,

Zhang,

Shao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Attention-Based Deep Convolutional Capsule Network for Hyperspectral Image Classification

Xiaoxia,

Xia

2024

IEEE Access

View full text Add to dashboard Cite

Hyperspectral remote sensing image analysis employing deep learning (DL) models has consistently demonstrated remarkable performance, owing to their robust nonlinear modeling and end-to-end optimization capabilities. Notably, the capsule neural network (CapsNet) has attracted substantial attention for its proficient feature extraction capabilities. However, it tends to overlook the inherent spatial heterogeneity within patch features. In this paper, we introduce a spatial attention-based deep convolutional capsule network (SA-CapsNet) to enhance CapsNet's performance in hyperspectral image (HSI) classification. The incorporation of a more potent and light-weight spatial attention mechanism introduces diversity among neighboring pixels. Additionally, we enhance the stability of learned spectral-spatial features by implementing a convolutional capsule layer that extends dynamic routing with 3D convolution. Experimental results conducted on three commonly used hyperspectral datasets demonstrate that SA-CapsNet outperforms conventional and state-of-the-art DL-based HSI classification algorithms in terms of classification accuracy and computational efficiency.

show abstract

Hierarchical capsule network for hyperspectral image classification

Cited by 2 publications

References 40 publications

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Attention-Based Deep Convolutional Capsule Network for Hyperspectral Image Classification

Contact Info

Product

Resources

About