Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification

By Chao Tao, H. Pan, Yansheng Li, Zhen-Xin Zou. IEEE Geoscience and Remote Sensing Letters, 2015

377 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/c317404601e9663c42887d7e1a6d4983365e1c69

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Citations in the corpus

Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification (2017, background): “The DBN- or SAEbased methods allow the unsupervised learning, and thus in this paper, we mainly investigate the DBN for its suitability to hyperspectral image classification.”
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, background): “Later, an improved autoencoder was proposed based on sparse constraint [50].”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, methodology): “To properly train systems, DL researchers employ a variety of strategies, including transfer learning [186–192], data augmentation, GAN [148,155], AE, and SSAE [193].”
Deep Learning for Hyperspectral Image Classification: An Overview (2019, methodology): “Recently, more and more research works [47], [97], [104], [105], [111], [116], [117] focus on designing a robust and effective unsupervised/semisupervised feature learning framework based on deep learning to classify HSIs.”
Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, background): “And the research in [13] introduced the stacked autoencoder to extract spatial informative features for hyperspectral images classification.”
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, methodology): “Similarly, in [170,171] a SAE was used, followed respectively by a SVM and a Multi Layer Perceptron (MLP) for the classiﬁcation.”
Deep learning in remote sensing: a review (2017, methodology): “[24] use sparse SAEs to learn an effective feature representation from input data; then, the learned features are fed into a linear SVM for hyperspectral data classification.”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “In [91] and [92], the local cubes from the first principal”
Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022, neutral): “[17], [18], [19], [20], autoencoder [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], and deep belief networks [31].”
Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification (2017, positive): “and spectral features for HSI classification [27].”
Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network (2019, methodology): “Similarly, Tao [22] used two sparse SAEs to capture spectral and spatial information separately.”
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning (2017, methodology): “Arguably, the most successful of these learned feature extraction methods for remote sensing imagery is the stacked autoencoder (Liu et al., 2015; Lin et al., 2013; Zhao et al., 2015; Ma et al., 2015; Tao et al., 2015).”