Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

By Yushi Chen, Xing Zhao, Xiuping Jia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015

1,042 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f9d119346b0773ea83251598fa5305bc75bac8ab

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Citations in the corpus

Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, neutral): “K 3 = This descriptor is then processed by a deep classifier, often unsupervised, such as deep belief networks in [55] and [56], restricted Boltzmann machines in [57] and [58], or cascades of autoencoders in [59]–[62].”
Deep learning classifiers for hyperspectral imaging: A review (2019, methodology): “In this regard, available DNN architectures are able to process both features by including spatial information as concatenated information to the spectral vector (following the traditional ML vector vision (Chen et al., 2014b; Chen et al., 2015)), or by processing the 3dimensional cube to maintain t”
Hyperspectral Image Classification With Deep Feature Fusion Network (2018, background): “In addition, a deep belief network (DBN), as another deep model, is also proposed for HSI classification [41], [42].”
Going Deeper With Contextual CNN for Hyperspectral Image Classification (2016, background): “feature extraction and classification in [1] and [7].”
Deep Few-Shot Learning for Hyperspectral Image Classification (2019, background): “deep belief networks [35], recurrent neural networks [36], 1-D CNN [37], 2-D CNN [38]–[41], and 3-D CNN [42]–[44]”
Deep Learning for Hyperspectral Image Classification: An Overview (2019, background): “In order to use DBN as a classifier, one can add a logistic regression layer at the end of the network to form a spectral classifier.”
Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks (2016, methodology): “It has been applied to remote sensing image processing [10], [11] and classification [12], [13], while convolutional neural networks as one of the typical deep learning models have achieved impressive performances in various fields [11], [13].”
Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network (2017, neutral): “applied unsupervised deep feature learning—including stacked autoencoder (SAE) [28] and deep brief network (DBN) [31]—for spectral–spatial feature extraction and classification.”
Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework (2018, background): “Furthermore, multilayer neural networks can extract robust and discriminative features of HSI and outperform SVMs [17], [18].”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, neutral): “details with spectral vector [44], [109] or process the 3D HSI”
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, background): “In 2015, another deep model, entitled deep belief network (DBN), was proposed [30].”
Hyperspectral Image Classification Using Deep Pixel-Pair Features (2017, methodology): “In [21], spectral–spatial features were obtained via deep belief network and the classification procedure was implemented by logistic regression.”
Big Data for Remote Sensing: Challenges and Opportunities (2016, methodology): “…and classification tasks when analyzing remote sensing images, in most of the works conducted so far only spectral features or the transformed spectral features (e.g., principal components) are used as inputs to the DNNs to generate a “better” representation of remote sensing images [38]–[40].”
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks (2016, background): “Works that include more spatial information in the neighborhood of the pixel to be classified have been done [34–40].”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “Another deep model, the deep belief network, was proposed for the classification of hyperspectral data through the learning of spectral-based features [100].”