Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis

Pan, Lei; Li, Heng-Chao; Deng, Yang‐Jun; Zhang, Fan; Chen, Xiangdong; Du, Qian

doi:10.3390/rs9050452

Cited by 46 publications

(23 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies have obtained promising results by using these techniques [8][9][10][11]26]. However, hyperspectral data redundancy is a big problem because of the high spectral dimensions and large number of bands [28]. In addition, the correlation between the spectral and AGB vary with the crop growth period, which is related to the physiological state of the crop [17].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

View full text Add to dashboard Cite

Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R 2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

show abstract

Section: Introductionmentioning

confidence: 99%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The proposed TDA-CFR is a tensor discriminative analysis based dimensionality reduction method, some related methods, including Linear discriminant analysis (LDA), sparse and low rank graph for discriminant analysis (SLGDA) [30] which employs the sparse and low rank constraints to achieve dimensionality under the graph embedding framework, low rank tensor approximation (LRTA) [31] in which the tensor low rank decomposition criterion is directly implemented on the raw hyperspectral images, group tensor based low rank decomposition (GTLR) [32] in which the tensor samples of hyperspectral images are firstly grouped into some clusters and then the tensor low rank decomposition is implemented on the obtained clusters, tensor discriminant analysis without compact representation (TDA) is also chosen as the comparison method to evaluate the effect of compact feature representation, tensor sparse and low rank graph based discriminant analysis (TSLGDA) [33] which is a tensor graph method with sparse and low rank constraints and the raw spectral feature (Original) is chosen as the benchmark.…”

Section: Methodsmentioning

confidence: 99%

Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

Song

Guo

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information of hyperspectral images has caused more and more concern in the field of hyperspectral images processing. In general, a desirable low dimensionality feature representation should be discriminative and compact. To achieve this, a tensor discriminant analysis model via compact feature representation (TDA-CFR) was proposed in this paper. In TDA-CFR, the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more informative and discriminative. Furthermore, TDA-CFR redefines the feature representation of each spectral band by employing the tensor low rank decomposition framework which leads to a more compact representation.

show abstract

“…A hyperspectral image [1][2][3][4] contains hundreds of continuous narrow spectral bands, spanning the visible to infrared spectrum. Hyperspectral sensors have attracted much interest in remote sensing for providing abundant and valuable information over the last few decades.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

He¹,

Liu²,

Wang³

et al. 2017

Remote Sensing

147

View full text Add to dashboard Cite

Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method, especially with a limited number of labeled samples.

show abstract

Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis

Cited by 46 publications

References 35 publications

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Contact Info

Product

Resources

About