A Fast and Compact 3-D CNN for Hyperspectral Image Classification

@article{Ahmad2020AFA,
  title={A Fast and Compact 3-D CNN for Hyperspectral Image Classification},
  author={Muhammad Ahmad and A. Khan and Manuel Mazzara and Salvatore Distefano and Mohsin Ali and Muhammad Shahzad Sarfraz},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2020},
  volume={19},
  pages={1-5}
}
Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI classification (HSIC) is a challenging task due to high interclass similarity, high intraclass variability, overlapping, and nested regions. The 2-D convolutional neural network (CNN) is a viable classification approach since HSIC depends on both spectral–spatial information. The 3-D CNN is a good alternative for improving the accuracy of HSIC, but it can be computationally intensive due to the volume and… 

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References

SHOWING 1-10 OF 32 REFERENCES

Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

A 3D convolutional neural network framework is proposed for accurate HSI classification, which is lighter, less likely to over-fit, and easier to train, and requires fewer parameters than other deep learning-based methods.

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated.

An improved hybrid CNN for hyperspectral image classification

  • Yuting LiLin He
  • Environmental Science, Computer Science
    International Conference on Graphic and Image Processing
  • 2020
An improved hybrid CNN to enhance the classification performance, which involves global average pooling, skip connection and appropriate adjustments of the convolution kernels and overall structure is proposed.

HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification

A hybrid spectral CNN (HybridSN) for HSI classification is proposed that reduces the complexity of the model compared to the use of 3-D-CNN alone and is compared with the state-of-the-art hand-crafted as well as end-to-end deep learning-based methods.

HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image

A novel convolutional neural network framework for the characteristics of hyperspectral image data called HSI-CNN, which can also provides ideas for the processing of one-dimensional data.

Multi-scale 3D deep convolutional neural network for hyperspectral image classification

  • Mingyi HeBo LiHuahui Chen
  • Computer Science, Environmental Science
    2017 IEEE International Conference on Image Processing (ICIP)
  • 2017
A Multiscale 3D deep convolutional neural network (M3D-DCNN) is proposed for HSI classification, which could jointly learn both 2D Multi-scale spatial feature and 1D spectral feature from HSI data in an end-to-end approach, promising to achieve better results with large-scale dataset.

Dual-Path Siamese CNN for Hyperspectral Image Classification With Limited Training Samples

  • Lin HuangYushi Chen
  • Environmental Science, Computer Science
    IEEE Geoscience and Remote Sensing Letters
  • 2021
The proposed classification framework is a combination of extended morphological profiles, CNN, siamese network, and spectral–spatial feature fusion and results reveal that the proposed methods provide the competitive results in terms of classification accuracy, especially with limited training samples.

3-D Deep Learning Approach for Remote Sensing Image Classification

The aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process.

A semi-supervised convolutional neural network for hyperspectral image classification

A novel semi-supervised convolutional neural network is proposed for the classification of hyperspectral image that can automatically learn features from complex hyperspectRAL image data structures and simultaneously minimize the sum of supervised and unsupervised cost functions.

Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification

  • L. ShenSen Jia
  • Environmental Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2011
A 3-D Gabor-wavelet-based approach for pixel-based hyperspectral imagery classification and results show that the method achieves as high as 96.04% and 95.36% accuracies, respectively, even when only few samples per class are labeled.