A dense convolutional neural network for hyperspectral image classification

Lu, Zhi; Yu, Xi; Liu, Bing; Wei, Xiangpo

doi:10.1080/2150704x.2018.1526424

Cited by 18 publications

(8 citation statements)

References 12 publications

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“…The accuracy for the classification sets of all models was over 99%. The excellent performance of the models demonstrated the potential of deep spectral features to represent the original data information, which has also been confirmed by previous studies [ 14 , 35 , 36 , 37 , 38 ]. Although the excellent classification of the original data has already been achieved, feature extraction and fusion strategy also obtained similarly outstanding results, which indicates that these are feasible strategies that could be explored further in future research.…”

Section: Resultssupporting

confidence: 84%

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Xiao

Bai

Gao

et al. 2020

Sensors

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Radix Astragali is a prized traditional Chinese functional food that is used for both medicine and food purposes, with various benefits such as immunomodulation, anti-tumor, and anti-oxidation. The geographical origin of Radix Astragali has a significant impact on its quality attributes. Determining the geographical origins of Radix Astragali is essential for quality evaluation. Hyperspectral imaging covering the visible/short-wave near-infrared range (Vis-NIR, 380–1030 nm) and near-infrared range (NIR, 874–1734 nm) were applied to identify Radix Astragali from five different geographical origins. Principal component analysis (PCA) was utilized to form score images to achieve preliminary qualitative identification. PCA and convolutional neural network (CNN) were used for feature extraction. Measurement-level fusion and feature-level fusion were performed on the original spectra at different spectral ranges and the corresponding features. Support vector machine (SVM), logistic regression (LR), and CNN models based on full wavelengths, extracted features, and fusion datasets were established with excellent results; all the models obtained an accuracy of over 98% for different datasets. The results illustrate that hyperspectral imaging combined with CNN and fusion strategy could be an effective method for origin identification of Radix Astragali.

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Section: Resultssupporting

confidence: 84%

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Xiao

Bai

Gao

et al. 2020

Sensors

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“…In the last few years, RS-HSI research has been particularly focused on this kind of architectures. Densenet-like architectures and VGG16 were also exploited in [ 135 , 156 ], respectively, for classification. In [ 158 ], Liu et al described a 3-D CNN trained via deep few-shot learning [ 167 ] to learn a metric space that causes the samples of the same class to be close to each other.…”

Section: Deep Learning Approaches To Hsimentioning

confidence: 99%

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

et al. 2019

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Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

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“…GRBS, an unsupervised band selection method based on graph representation, can perform better in both accuracy and efficiency. The spatial neighborhood of each pixel is set to 9 × 9 with reference to [25,39,48]. After the above processing, each HSI is transformed into a number of 9 × 9 × 100 data cubes, so as to standardize the data dimensions and optimize the learning process.…”

Section: Source Data Setsmentioning

confidence: 99%

“…Yue et al take the lead in exploring the effect of 2D-CNN in HSI classification. Subsequently, many improved models based on 2D-CNN have been proposed and refresh classification accuracy constantly, such as DR-CNN [23], contextual deep CNN [24], DCNN [25], DC-CNN [26], and so on. Most 2D-CNN-based methods use PCA to reduce the dimension of HSI in order to reduce the number of channels in the convolution operation.…”

Section: Introductionmentioning

confidence: 99%

Deep Relation Network for Hyperspectral Image Few-Shot Classification

et al. 2020

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Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.

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A dense convolutional neural network for hyperspectral image classification

Cited by 18 publications

References 12 publications

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Deep Relation Network for Hyperspectral Image Few-Shot Classification

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