Discriminant Tensor Spectral–Spatial Feature Extraction for Hyperspectral Image Classification

Zhi, Zhao; Fan, Bin; Duan, Jiangyong; Wang, Lingfeng; Ding, Kun; Xiang, Shiming; Chen, Pan

doi:10.1109/lgrs.2014.2375188

Cited by 78 publications

(15 citation statements)

References 22 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that recent study in [34] is related to our work. There are, however, three major conceptual differences.…”

supporting

confidence: 59%

“…In addition, natural images are usually generated by the interaction of multiple factors related to scene structure, illumination and imaging [33]. Recently, tensor decomposition has shown great potentials for HSI classification [34][35][36], denosing [37], dimensionality reduction [38], hyperspectral and multispectral image fusion [39], target detection [40,41], spectral unmixing [42], etc. However, previous tensor factorization related studies rarely exploited hyperspectral and LiDAR data fusion and classification.…”

mentioning

confidence: 99%

“…First, we focus on hyperspectral and LiDAR data fusion by using third-order tensor factorization based on morphological features, whereas in [34], morphological feature extraction and tensor discriminant analysis were integrated for HSI classification. Second, our work models the extracted spectral-spatial features as third-order tensors, whereas the work [34] rearranged the features into second-order tensors, which actually is still in flat-view matrix style. Third, we conduct coupled tensor factorization based on multiple tensors, whereas the work [34] actually belongs to matrix factorization.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

et al. 2019

View full text Add to dashboard Cite

Hyperspectral and light detection and ranging (LiDAR) data fusion and classification has been an active research topic, and intensive studies have been made based on mathematical morphology. However, matrix-based concatenation of morphological features may not be so distinctive, compact, and optimal for classification. In this work, we propose a novel Coupled Higher-Order Tensor Factorization (CHOTF) model for hyperspectral and LiDAR data classification. The innovative contributions of our work are that we model different features as multiple third-order tensors, and we formulate a CHOTF model to jointly factorize those tensors. Firstly, third-order tensors are built based on spectral-spatial features extracted via attribute profiles (APs). Secondly, the CHOTF model is defined to jointly factorize the multiple higher-order tensors. Then, the latent features are generated by mode-n tensor-matrix product based on the shared and unshared factors. Lastly, classification is conducted by using sparse multinomial logistic regression (SMLR). Experimental results, conducted with two popular hyperspectral and LiDAR data sets collected over the University of Houston and the city of Trento, respectively, indicate that the proposed framework outperforms the other methods, i.e., different dimensionality-reduction-based methods, independent third-order tensor factorization based methods, and some recently proposed hyperspectral and LiDAR data fusion and classification methods.

show abstract

“…It should be noted that recent study in [34] is related to our work. There are, however, three major conceptual differences.…”

supporting

confidence: 59%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Tensor analysis is a multilinear algebra tool which needs no vectoring operation. Tensor analysis has been widely considered in hyperspectral image processing and achieved promising performance [16][17][18][19][20][21]. In tensor based methods, the spatial and spectral information are preserved simultaneously by representing hyperspectral images in the form of 3-order tensors.…”

Section: Introductionmentioning

confidence: 99%

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Lei

Song

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches.

show abstract

“…Nie et al proposed a local within class scatter matrix criterion which overcome the drawback of Gaussian hypothesis in LDA [22]. Zhong et al proposed an integrated spatial-spectral feature extract method for original hyperspectral images under the tensor analysis framework which can characterize the intrinsic formation of the original hyperspectral images more efficiently [23]. By constructing the same class and different class patch with tensor samples, Zhang et al proposed the discriminative analysis by maximizing the distances of different class while minimizing the distances of different class [19].…”

Section: Introductionmentioning

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

Discriminant Tensor Spectral–Spatial Feature Extraction for Hyperspectral Image Classification

Cited by 78 publications

References 22 publications

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

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

Contact Info

Product

Resources

About