Journal ArticleDOI
Hyperspectral Imagery Denoising by Deep Learning With Trainable Nonlinearity Function
Weiying Xie,Yunsong Li +1 more
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TLDR
A deep stage convolutional neural network with trainable nonlinearity functions is applied for the first time to remove noise in HSIs and the experimental results confirm that the proposed method can obtain a more effective and efficient performance.Abstract:
Hyperspectral images (HSIs) can describe subtle differences in the spectral signatures of objects, and thus they are effective in a wide array of applications. However, an HSI is inevitably contaminated with some unwanted components like noise resulting in spectral distortion, which significantly decreases the performance of postprocessing. In this letter, a deep stage convolutional neural network (CNN) with trainable nonlinearity functions is applied for the first time to remove noise in HSIs. Besides the fact that the weight and bias matrices are learned from cubic training clean-noisy HSI patches, the nonlinearity functions in each stage are also trainable, which differ from the conventional CNN with a fixed nonlinearity function. Compared with the state-of-the-art HSI denoising methods, the experimental results on both synthetic and real HSIs confirm that the proposed method can obtain a more effective and efficient performance.read more
Citations
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Journal ArticleDOI
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network.
TL;DR: Wang et al. as discussed by the authors proposed a novel deep learning-based method by learning a non-linear end-to-end mapping between the noisy and clean HSIs with a combined spatial-spectral deep convolutional neural network (HSID-CNN).
Journal ArticleDOI
Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network
TL;DR: In this paper, a unified spatial-temporal-spectral framework based on a deep convolutional neural network (CNN) was proposed for missing information reconstruction in remote sensing images.
Journal ArticleDOI
Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual Convolutional Neural Network
TL;DR: A novel deep learning-based method by learning a nonlinear end-to-end mapping between the noisy and clean HSIs with a combined spatial–spectral deep convolutional neural network (HSID-CNN) that outperforms many of the mainstream methods in both the quantitative evaluation indexes, visual effects, and HSI classification accuracy.
Journal ArticleDOI
Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review
TL;DR: This paper reviews the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and introduces the available data sources and datasets used by literature studies to provide the readers with a framework to interpret the-state-of-the-art ofdeep learning in this context.
Journal ArticleDOI
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial–Spectral Gradient Network
TL;DR: In this article, a spatial-spectral gradient network (SSGN) is proposed for mixed noise removal in hyperspectral images. But the proposed method employs a spatial gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for effectively extracting intrinsic and deep features of HSIs.
References
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Proceedings Article
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Hyperspectral Remote Sensing Data Analysis and Future Challenges
Jose M. Bioucas-Dias,Antonio Plaza,Gustau Camps-Valls,Paul Scheunders,Nasser M. Nasrabadi,Jocelyn Chanussot +5 more
TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.
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TL;DR: These experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization.
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