Hyperspectral data compression using sparse representation

Huo, Chengfu; Zhang, Rong; Yin, Dong; Wu, Qian; Xu, Dawei

doi:10.1109/whispers.2012.6874259

Cited by 7 publications

(6 citation statements)

References 10 publications

(11 reference statements)

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“…Recently, sparse representation paradigm [7] has been intensively investigated, demonstrating its effectiveness in several fields such as multi-class feature selection [50], image restoration [9], data compression [14], visual tracking [21,48], image classification [41,44] and, not the least, face recognition systems.…”

Section: Related Workmentioning

confidence: 99%

Robust face recognition using sparse representation in LDA space

Adamo

Grossi

Lanzarotti

et al. 2015

Machine Vision and Applications

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In this article, we address the problem of face recognition under uncontrolled conditions. The proposed solution is a numerical robust algorithm dealing with face images automatically registered and projected via the linear discriminant analysis (LDA) into a holistic low-dimensional feature space. At the heart of this discriminative system, there are suitable nonconvex parametric mappings based on which a fixed-point technique finds the sparse representation of test images allowing their classification. We theoretically argue that the success achieved in sparsity promoting is due to the sequence of values imposed on a characteristic parameter of the used mapping family. Experiments carried out on several databases (ORL, YaleB, BANCA, FRGC v2.0) show the robustness and the ability of the system for classification purpose. In particular, within the area of sparsity promotion, our recognition system shows very good performance with respect to those achieved by the stateof-the-art 1 norm-based sparse representation classifier (SRC), the recently proposed 2 norm-based collaborative representation classifier (CRC), the LASSO-based sparse decomposition technique, and the weighted sparse represen-B Raffaella Lanzarotti tation method (WSRC), which integrates sparsity and data locality structure.

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Section: Related Workmentioning

confidence: 99%

Robust face recognition using sparse representation in LDA space

Adamo

Grossi

Lanzarotti

et al. 2015

Machine Vision and Applications

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“…The proposed hyperspectral compression method based on sparse coding with online learning by using BP is tested with the data in Table 3.1 in order to obtain its rate-distortion performance. Several state-of-the-art hyperspectral compression methods in the literature [23][24][25][26][27][28] are utilized in the performance comparison. Moreover, hyperspectral data compression method based on sparse coding with online learning by using Least Angle Regression (LARS) in [1] is also compared with the proposed BP based compression scheme.…”

Section: Compression Performance Analysismentioning

confidence: 99%

“…In Figure 3.1, rate-distortion performance of the proposed method is compared with the performances of 3D-SPIHT (Set Partitioning in Hierarchical Trees) algorithm, JOMP (Joint Orthogonal Matching Pursuit) algorithm with n=1024, FJOMP (Fast Joint Orthogonal Matching Pursuit) algorithm with n=256 [25] and sparse coding with online learning algorithms by using LARS [1]. The hyperspectral data used in the comparison is Cuprite image whose attributes are presented in Table 3.1.…”

Section: Compression Performance Analysismentioning

confidence: 99%

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Hyperspectral image compression using an online learning method

Ülkü¹,

Töreyi̇n

2015

Satellite Data Compression, Communications, and Processing XI

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A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.

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“…Classification of lossy and lossless compression methods is canonically fourfold: prediction-based [24,31], transformation-based [9,28], vector quantization (VQ)based [32], and sparse representation-based [18]. One of the transformation-based algorithms is the principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

Lossy Compressive Sensing Based on Online Dictionary Learning

Ülkü

Kızgut

2019

cai

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In this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance.

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Hyperspectral data compression using sparse representation

Cited by 7 publications

References 10 publications

Robust face recognition using sparse representation in LDA space

Robust face recognition using sparse representation in LDA space

Hyperspectral image compression using an online learning method

Lossy Compressive Sensing Based on Online Dictionary Learning

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