Spatial-spectral local discriminant projection for dimensionality reduction of hyperspectral image

Hong, Huixiao; Duan, Yule; He, Haibo; Shi, Guangyao; Luo, Fulin

doi:10.1016/j.isprsjprs.2019.06.018

Cited by 18 publications

(20 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have shown that there is a high potential for models that incorporate feature extracting abilities as well as capabilities of modeling non-linear phenomena in statistical retrieval. Exploring a greater variety of dimensionality reduction techniques, such as the spatio-spectral supervised technique presented in [42], or incorporating it into the CNN architecture is also an area to further explore. Finding optimal architectures for CNNs remains an open task in the deep learning literature, and due to the non-convexity of the problem, experiments are the only way to find optimal models.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Statistical retrieval of atmospheric profiles with deep convolutional neural networks

Malmgren-Hansen

Laparra

Camps‐Valls

2019

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

Section: Conclusion and Discussionmentioning

confidence: 99%

Statistical retrieval of atmospheric profiles with deep convolutional neural networks

Malmgren-Hansen

Laparra

Camps‐Valls

2019

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

“…To prove the effectiveness, DFCEN is compared with several dimensionality reduction algorithms, such as LE [11], LLE [11], SAE, spatial-domain local pixel NPE (LPNPE) [38], spatial and spectral regularized local discriminant embedding (SSRLDE) [38], SSMRPE [39], spatial-spectral local discriminant projection (SSLDP) [40]. The former three methods are spectral-based methods while the latter four approaches make use of both spatial and spectral information for dimensionality reduction of HSIs.…”

Section: Methodsmentioning

confidence: 99%

“…SSMRPE [39] shares the same DR concept as LLE. SSLDP [40] designs a weighted within neighborhood scatter to reveal the similarity of spatial neighbors. Among them, SSRLDE [38] and SSLDP [40] are supervised and require class labels to implement dimensionality reduction, while others are unsupervised.…”

Section: Methodsmentioning

confidence: 99%

“…For the fairness of the experimental comparison, the numbers of the nearest neighbor samples k of LE and LLE are the same as that of DFCEN_LE and DFCEN_LLE in the following experiments. We also choose the optimal parameters of their source literature for LPNPE [38], SSRLDE [38], SSMRPE [39], SSLDP [40]. In all the experiments below, all algorithms including DFCEN use raw data (that is not filtered to de-noise and smoothen pixels).…”

Section: Methodsmentioning

confidence: 99%

“…As we have seen, the spatial-spectral combined algorithm, LPNPE [38], SSRLDE [38], SSMRPE [39], SSLDP [40] and DFCEN, are superior to the spectral-based algorithm, LE, LLE and SAE, which indicates that spatial features are beneficial to the dimensionality reduction of HSIs. Neural network based methods, SAE and DFCEN, are superior to traditional dimensionality reduction algorithms, which testifies that neural network is suitable for dimensionality reduction of HSIs.…”

Section: Classification Performancementioning

confidence: 99%

See 2 more Smart Citations

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Zhou

Shi

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the class labels of HSIs are limited and difficult to obtain. While a few unsupervised CNN-based methods have been proposed recently, they always focus on data reconstruction and are lacking in the exploration of discriminability which is usually the primary goal of DR. To address these issues, we propose a deep fully convolutional embedding network (DFCEN), which not only considers data reconstruction but also introduces the specific learning task of enhancing feature discriminability. DFCEN has an end-to-end symmetric network structure that is the key for unsupervised learning. Moreover, a novel objective function containing two terms—the reconstruction term and the embedding term of a specific task—is established to supervise the learning of DFCEN towards improving the completeness and discriminability of low-dimensional data. In particular, the specific task is designed to explore and preserve relationships among samples in HSIs. Besides, due to the limited training samples, inherent complexity and the presence of noise in HSIs, a preprocessing where a few noise spectral bands are removed is adopted to improve the effectiveness of unsupervised DFCEN. Experimental results on three well-known hyperspectral datasets and two classifiers illustrate that the low dimensional features of DFCEN are highly separable and DFCEN has promising classification performance compared with other DR methods.

show abstract

Image Projection Space Invariant Technology

Wang

Gao

2020

Application of Intelligent Systems in Multi-Modal Information Analytics

View full text Add to dashboard Cite

Spatial-spectral local discriminant projection for dimensionality reduction of hyperspectral image

Cited by 18 publications

References 39 publications

Statistical retrieval of atmospheric profiles with deep convolutional neural networks

Statistical retrieval of atmospheric profiles with deep convolutional neural networks

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Image Projection Space Invariant Technology

Contact Info

Product

Resources

About