Corpus ID: 231627645

Hyperspectral Image Classification - Traditional to Deep Models: A Survey for Future Prospects

@article{Shabbir2021HyperspectralIC,
  title={Hyperspectral Image Classification - Traditional to Deep Models: A Survey for Future Prospects},
  author={Sidrah Shabbir and Muhammad Ahmad},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.06116}
}
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor that effectively… Expand
Regularized CNN Feature Hierarchy for Hyperspectral Image Classification
TLDR
It is empirically show that, in improving generalization performance, regularization also improves model calibration, which significantly improves beam-search and helps to prevent CNN from becoming over-confident. Expand
3D/2D regularized CNN feature hierarchy for Hyperspectral image classification
TLDR
An idea to enhance the generalization performance of a hybrid CNN for HSIC using soft labels that are a weighted average of the hard labels and uniform distribution over ground labels is proposed to prevent CNN from becoming over-confident. Expand
Deep global-local transformer network combined with extended morphological profiles for hyperspectral image classification
  • Xiong Tan, Kuiliang Gao, Bing Liu, Yumeng Fu, Lei Kang
  • Engineering
  • 2021
Abstract. Recently, deep learning models based on convolutional neural networks (CNN) remain dominant in hyperspectral image (HSI) classification. However, there are some problems in CNN models, suchExpand
Artifacts of different dimension reduction methods on hybrid CNN feature hierarchy for Hyperspectral Image Classification
TLDR
This work presents a compact hybrid CNN model which overcomes the aforementioned challenges by distributing spatial–spectral feature extraction across 3D and 2D layers and shows that the proposed pipeline outperformed in terms of generalization performance and statistical significance. Expand
Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification
  • Feng Zhao, Junjie Zhang, Zhe Meng, Hanqiang Liu
  • Computer Science
  • Remote. Sens.
  • 2021
TLDR
Experiments indicate that PDCNet proposed in this paper has good classification performance compared with other popular models, and dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Expand
Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review
TLDR
Machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense. Expand
Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images
TLDR
The application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs with experimental evaluation indicates that the proposed semisupervised model achieves reliable classification. Expand
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
TLDR
This work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Expand

References

SHOWING 1-10 OF 322 REFERENCES
Deep Learning for Hyperspectral Image Classification: An Overview
TLDR
This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Expand
A survey: Deep learning for hyperspectral image classification with few labeled samples
TLDR
Although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. Expand
Deep learning classifiers for hyperspectral imaging: A review
TLDR
A comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature is provided, providing an exhaustive comparison of the discussed techniques. Expand
A Lightweight Convolutional Neural Network for Hyperspectral Image Classification
TLDR
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. Expand
3-D Deep Learning Approach for Remote Sensing Image Classification
TLDR
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. Expand
Dimensionally Reduced Features for Hyperspectral Image Classification Using Deep Learning
TLDR
This chapter analyzes the effect of dimensionality reduction on vectorized convolution neural networks (VCNNs) for HSI classification using a VCNN, and comparable classification accuracy is obtained using the reduced feature dimension and a lesser number of VCNN trainable parameters. Expand
Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection
TLDR
A semi-supervised deep learning framework based on the residual networks (ResNets) which can successfully guide ResNets to learn from the unlabeled data by making full use of the complementary cues of the spectral and spatial features in HSI classification. Expand
Hyperspectral image classification via a random patches network
TLDR
This study proposes an efficient deep learning based method, namely, Random Patches Network (RPNet) for HSI classification, which directly regards the random patches taken from the image as the convolution kernels without any training. Expand
Hyperspectral image reconstruction by deep convolutional neural network for classification
TLDR
Experimental results indicate that framework built based on CNN and ELM provides competitive performance with small number of training samples, and the average accuracy of ELM can be improved as high as 30.04%, while performs tens to hundreds of times faster than those state-of-the-art classifiers. Expand
Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification
TLDR
A new three-channel image build method by which the trained networks on natural images are used to extract the spatial features and a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Expand
...
1
2
3
4
5
...