scholarly journals Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction

2019 ◽  
Vol 11 (2) ◽  
pp. 193 ◽  
Author(s):  
Jize Xue ◽  
Yongqiang Zhao ◽  
Wenzhi Liao ◽  
Jonathan Chan
Keyword(s):  
Remote Sensing ◽  
Compressive Sensing ◽  
Implementation Strategy ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Low Rank ◽  
Structured Sparsity ◽  
Spectral Correlation ◽  
Tensor Nuclear Norm ◽  
Nonlocal Structure

Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the l 1 -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery.

scholarly journals Approximate Sparse Regularized Hyperspectral Unmixing

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Chengzhi Deng ◽  
Yaning Zhang ◽  
Shengqian Wang ◽  
Shaoquan Zhang ◽  
Wei Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optimization Model ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Hyperspectral Unmixing ◽  
Variable Splitting

Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser thanl1regularizer and much easier to be solved thanl0regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison tol1regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-artl1regularizer.

scholarly journals Compressive Sensing of Multichannel EEG Signals via lq Norm and Schatten-p Norm Regularization

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Jun Zhu ◽  
Changwei Chen ◽  
Shoubao Su ◽  
Zinan Chang
Keyword(s):  
Compressive Sensing ◽  
Optimization Model ◽  
State Of The Art ◽  
Rank Function ◽  
Nuclear Norm ◽  
Low Rank ◽  
Eeg Signals ◽  
Regularization Problem ◽  
Surrogate Function ◽  
Multichannel Eeg

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Recently, a simultaneous cosparsity and low-rank (SCLR) optimization model has shown the state-of-the-art performance in compressive sensing (CS) recovery of multichannel EEG signals. How to solve the resulting regularization problem, involving l0 norm and rank function which is known as an NP-hard problem, is critical to the recovery results. SCLR takes use of l1 norm and nuclear norm as a convex surrogate function for l0 norm and rank function. However, l1 norm and nuclear norm cannot well approximate the l0 norm and rank because there exist irreparable gaps between them. In this paper, an optimization model with lq norm and schatten-p norm is proposed to enforce cosparsity and low-rank property in the reconstructed multichannel EEG signals. An efficient iterative scheme is used to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform existing state-of-the-art CS methods for compressive sensing of multichannel EEG channels.

scholarly journals NON-LINEAR AUTOENCODER BASED ALGORITHM FOR DIMENSIONALITY REDUCTION OF AIRBORNE HYPERSPECTRAL DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 593-598
Author(s):  
S. Priya ◽  
R. Ghosh ◽  
B. K. Bhattacharya
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Hyperspectral Data ◽  
Correlation Technique ◽  
Spectral Correlation ◽  
Spectral Angle Mapper ◽  
Non Linear

<p><strong>Abstract.</strong> Hyperspectral remote sensing is an advanced remote sensing technology that enhances the ability of accurate classification due to presence of narrow contiguous bands. The large number of continuous bands present in hyperspectral data introduces the problem of computational complexity due to presence of redundant information. There is a need for dimensionality reduction to enhance the ability of users for better characterization of features. Due to presence of high spectral correlation in the hyperspectral datasets, optimum de-correlation technique is required which transforms the hyperspectral data to lower dimensions without compromising with the desirable information present in the data. In this paper, focus has been to reduce the spectral dimensionality problem. So, this research aimed to develop computationally efficient non-linear autoencoder algorithm taking the advantage of non-linear properties of hyperspectral data. The proposed algorithm was applied on airborne hyperspectral image of Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) over Anand region of Gujarat and the performance of the algorithm was evaluated. The Signal-to-Noise Ratio (SNR) increased from 22.78 dB to 48.48 dB with increase in number of nodes in bottleneck layer for reconstruction of image. Spectral distortion was also measured using Spectral Angle Mapper Algorithm (SAM), which reduced from 0.38 to 0.05 with increase in number of nodes in bottleneck layer up to 10. So, this algorithm was able to give good reconstruction of original image from the nodes present in the bottleneck layer.</p>

scholarly journals A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Han Pan ◽  
Zhongliang Jing ◽  
Lingfeng Qiao ◽  
Minzhe Li
Keyword(s):  
Remote Sensing ◽  
Impulse Noise ◽  
State Of The Art ◽  
Problem Formulation ◽  
The Other ◽  
Low Rank ◽  
Experimental Comparison ◽  
Unified Framework ◽  
Sparsity Structure ◽  
Minimization Scheme

The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing. However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse component. In this paper, a new problem formulation with regular spectral k-support norm and regular k-support l1 norm is proposed. A unified framework is developed to capture the intrinsic sparsity structure of all two components. To address the resulting problem, an efficient minimization scheme within the framework of accelerated proximal gradient is proposed. This scheme is achieved by alternating regular k-shrinkage thresholding operator. Experimental comparison with the other state-of-the-art methods demonstrates the efficacy of the proposed method.

scholarly journals Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation

2020 ◽  
Vol 12 (14) ◽  
pp. 2264
Author(s):  
Hongyi Liu ◽  
Hanyang Li ◽  
Zebin Wu ◽  
Zhihui Wei
Keyword(s):  
Hyperspectral Image ◽  
Convex Relaxation ◽  
Principal Component ◽  
Nuclear Norm ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Rank Tensor ◽  
Alternating Direction ◽  
Tensor Approximation ◽  
Tensor Nuclear Norm

Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness, this paper develops a non-convex relaxation approach for low-rank tensor approximation. Firstly, a non-convex approximation of tensor nuclear norm (NCTNN) is introduced to the low-rank tensor completion. Secondly, a non-convex tensor robust principal component analysis (NCTRPCA) method is proposed, which aims at exactly recovering a low-rank tensor corrupted by mixed-noise. The two proposed models are solved efficiently by the alternating direction method of multipliers (ADMM). Three HSI datasets are employed to exhibit the superiority of the proposed model over the low rank penalization method in terms of accuracy and robustness.

scholarly journals Hyperspectral Image Denoising via Framelet Transformation Based Three-Modal Tensor Nuclear Norm

2021 ◽  
Vol 13 (19) ◽  
pp. 3829
Author(s):  
Wenfeng Kong ◽  
Yangyang Song ◽  
Jing Liu
Keyword(s):  
Fourier Transform ◽  
Image Quality ◽  
Hyperspectral Image ◽  
Convex Relaxation ◽  
Nuclear Norm ◽  
Low Rank ◽  
Tensor Rank ◽  
Spatial Dimensions ◽  
Value Decomposition ◽  
Tensor Nuclear Norm

During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step. In HSI denoising tasks, the method based on low-rank prior has achieved satisfying results. Among numerous denoising methods, the tensor nuclear norm (TNN), based on the tensor singular value decomposition (t-SVD), is employed to describe the low-rank prior approximately. Its calculation can be sped up by the fast Fourier transform (FFT). However, TNN is computed by the Fourier transform, which lacks the function of locating frequency. Besides, it only describes the low-rankness of the spectral correlations and ignores the spatial dimensions’ information. In this paper, to overcome the above deficiencies, we use the basis redundancy of the framelet and the low-rank characteristics of HSI in three modes. We propose the framelet-based tensor fibered rank as a new representation of the tensor rank, and the framelet-based three-modal tensor nuclear norm (F-3MTNN) as its convex relaxation. Meanwhile, the F-3MTNN is the new regularization of the denoising model. It can explore the low-rank characteristics of HSI along three modes that are more flexible and comprehensive. Moreover, we design an efficient algorithm via the alternating direction method of multipliers (ADMM). Finally, the numerical results of several experiments have shown the superior denoising performance of the proposed F-3MTNN model.

scholarly journals PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1243
Author(s):  
Wenxin Yin ◽  
Wenhui Diao ◽  
Peijin Wang ◽  
Xin Gao ◽  
Ya Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plants ◽  
State Of The Art ◽  
Thermal Power ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Thermal Power Plants ◽  
Average Precision ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

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