Semisupervised Neural Networks for Efficient Hyperspectral Image Classification

By Frédéric Ratle, Gustau Camps-Valls, Jason Aaron Edward Weston. IEEE Transactions on Geoscience and Remote Sensing, 2010

323 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/58c239e6c2c38dea061310ed5d719c3221d4428f

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Citations in the corpus

Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation (2014, background): “network [10], adaptive artificial immune network [11], and artificial deoxyribonucleic acid computing [12].”
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, background): “For example, in [193], Ratle et al. proposed a semi-supervised neural network framework for large scale HSI classiﬁcation.”
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods (2013, methodology): “More homogeneous areas and better classification maps are observed in general for the mean map and bag kernels, and particularly for the SSNN, which efficiently deals with complex marsh areas (south east area of the image) and cope with large scale datasets.”
Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, unknown): “This has been implemented since the 2000s [40], [41] with small networks, and it was brought up to date recently by [42] with unidimensional CNNs, which learn a filter collection to be applied on individual spectra.”
Learning multiscale and deep representations for classifying remotely sensed imagery (2016, methodology): “Bayesian models (Landgrebe, 2005), feature extraction and feature reduction techniques, neural networks (Ratle et al., 2010) and kernel methods (Schölkopf and Smola, 2002) have been investigated for the classification of hyperspectral images.”
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering (2014, background): “, random forests [7], [8], neural networks [9],”
Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification (2017, methodology): “Research in the last decades has resulted in many popular models, such as neural networks [7], [8], the Gaussian process approach [9], conditional random fields [10], [11], the graph method [12], [13], multinomial logistic regression [14], [15], support vector machines (SVMs) [16], AdaBoost [17], an”
Self-Supervised Learning in Remote Sensing: A review (2022, background): “…the remote sensing community, semi-supervised learning has been long studied and enjoys applications in, e.g., hyperspectral image recognition and processing [19, 20, 21, 22, 23, 24, 25, 26, 27, 28], multi-spectral image segmentation [29, 30, 31, 32, 33, 34, 35] and SAR-optical data fusion [36].”
Semi-Supervised and Unsupervised Extreme Learning Machines (2014, methodology): “The proposed SS-ELM is related to a few other manifold assumption based semi-supervised learning algorithms, such as the Laplacian support vector machines (LapSVMs) [47], the Laplacian regularized least squares (LapRLS) [47], semi-supervised neural networks (SSNNs) [48], and semisupervised deep embe”