Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

By Jaime Zabalza, Jinchang Ren, Jiangbin Zheng, Huimin Zhao, Chunmei Qing, Zhijing Yang, Peijun Du, Stephen Marshall. Neurocomputing, 2016

363 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9616ece30b7ed1972a28369e4723d02a86e5aa97

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Citations in the corpus

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017, methodology): “Zabalza et al.(211) used segmented SAEs that are used for dimensionality reduction.”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “A stacked Autoencoder (SAE) is a neural network built on the top of several layers of autoencoders where the output of each hidden layer is connected to the input of the next hidden layer.”
Breast cancer detection using deep convolutional neural networks and support vector machines (2019, methodology): “DCNN has achieved success in image classification problems including image analysis as in ( Han et al., 2015 ; Zabalza et al., 2016 ).”
High-quality hyperspectral reconstruction using a spectral prior (2017, methodology): “They have been used for spectral image classification [Chen et al. 2014; Lin et al. 2013; Ma et al. 2016; Xing et al. 2016; Zabalza et al. 2016] or denoising [Vincent et al.”
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, background): “ieee Geoscience and remote sensinG maGazine june 2016 28 and a stack of the AE [73], [75], [79], [80].”
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, methodology): “In [65], Alam et al. presented a technique that operates on a superpixel partitioning based on both spectral and spatial properties; in [66] the segmentation of the image was used as a preliminary step to focus the subsequent classiﬁcation on meaningful and well circumscribed regions.”
A review of deep learning used in the hyperspectral image analysis for agriculture (2021, background): “Also, the segmented SAE is proposed to reduce complexity but improve the efficiency of feature representation and the accuracy of hyper-spectral image classification (Jaime et al. 2016).”