• Corpus ID: 85499343

1D-Convolutional Capsule Network for Hyperspectral Image Classification

@article{Zhang20191DConvolutionalCN,
  title={1D-Convolutional Capsule Network for Hyperspectral Image Classification},
  author={Haitao Zhang and Lingguo Meng and Xian Wei and Xiaoliang Tang and Xuan Tang and Xingping Wang and Bo Jin and Wei Yao},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.09834}
}
Recently, convolutional neural networks (CNNs) have achieved excellent performances in many computer vision tasks. Specifically, for hyperspectral images (HSIs) classification, CNNs often require very complex structure due to the high dimension of HSIs. The complex structure of CNNs results in prohibitive training efforts. Moreover, the common situation in HSIs classification task is the lack of labeled samples, which results in accuracy deterioration of CNNs. In this work, we develop an easy… 
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15 Citations
Background Citations
5
Methods Citations
4

15 Citations

Spectral–Spatial Hyperspectral Image Classification Using Dual-Channel Capsule Networks

This letter proposes a new network architecture based on the CapsNet for HSI classification tasks, called dual-channel capsule network (DCCapsNet), which was trained and validated on four real HSI data sets and achieved high accuracy.

Multiscale Feature Aggregation Capsule Neural Network for Hyperspectral Remote Sensing Image Classification

This study proposes a multiscale feature aggregation capsule neural network (MS-CapsNet) based on CapsNet via the implementation of two branches that simultaneously extract spectral, local spatial, and global spatial features to integrate multiscales features and improve model robustness.

A Lightweight Convolutional Neural Network for Hyperspectral Image Classification

Experimental results illustrate that the developed LWCNN approach is advantageous in both the efficiency and robustness sides for hyperspectral image classification tasks and outperforms other state-of-the-art methods (both traditional-based and DL-based) with very limited labeled samples.

A non-local capsule neural network for hyperspectral remote sensing image classification

The proposed NLCapsNet can effectively enhance the classification accuracy with a rapid convergence speed and avoid overfitting when the number of training samples is limited.

Multi-stack hybrid CNN with non-monotonic activation functions for hyperspectral satellite image classification

This paper proposes a variant of the 3D-2D CNN Hybrid model to extract representational features from a different class of land use land cover using different receptive fields in a multi-stack arrangement and explores through experimentation the possible replacement of monotonic activation function ReLu using non-monotonic functions.

Capsule Networks - A survey

Deep Learning-Based Phenological Event Modeling for Classification of Crops

The proposed architecture, called the variational capsule network (VCapsNet), significantly improves the classification and denoising results and is less sensitive to noise and yields good results, even at shallower depths, compared to the main existing approaches.

Analysis of the proficiency of fully connected neural networks in the process of classifying digital images. Benchmark of different classification algorithms on high-level image features from convolutional layers

Deep Learning-Based Phenological Event Modeling for Classification of Crops

It was observed that the regularization of classification using the reconstruction loss and that of denoising using the label information priors improve the generalizability and convergence of the network, and this study illustrates the use of interpretability-based evaluation measures.

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

This paper is the first to use hyperspectral imaging (HSI) to implement the surface inspection of five wet-blue leather defects including brand masks, rotten grain, rupture, insect bites, and scratches in the pixel level detection and demonstrated that the overall performance of WBS was better than the original CEM.

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