GPU Parallel Optimization of Hyperspectral Image Kernel Sparse Representation Classification Based on Spatial-Spectral Graph Regularization

2016 ◽  
Author(s):  
Jida Zheng ◽  
Zebin Wu ◽  
Qicong Wang ◽  
Jianjun Liu ◽  
Zhihui Wei ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Parallel Optimization ◽  
Graph Regularization ◽  
Spectral Graph ◽  
Sparse Representation Classification

Robust patch-based sparse representation for hyperspectral image classification

2017 ◽  
Vol 15 (03) ◽  
pp. 1750028 ◽  
Author(s):  
Haoliang Yuan
Keyword(s):  
Sparse Representation ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Real Life ◽  
Test Sample ◽  
Classification Performance ◽  
Iteration Algorithm ◽  
Frobenius Norm ◽  
Training Samples ◽  
Sparse Representation Classification

Sparse representation classification (SRC) has been successfully applied into hyperspectral image (HSI). A test sample (pixel) can be linearly represented by a few training samples of the training set. The class label of the test sample is then decided by the reconstruction residuals. To incorporate the spatial information to improve the classification performance, a patch matrix, which includes a spatial neighborhood set, is used to replace the original pixel. Generally, the objective function of the reconstruction residuals is represented as Frobenius-norm, which actually treats the elements in the reconstruction residuals in the same way. However, when a patch locates in the image edge, the samples in the patch may belong to different classes. Frobenius-norm is not suitable to compute the reconstruction residuals. In this paper, we propose a robust patch-based sparse representation classification (RPSRC) based on [Formula: see text]-norm. An iteration algorithm is given to compute RPSRC efficiently. Extensive experimental results on two real-life HSI datasets demonstrate the effectiveness of RPSRC.

scholarly journals Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haifeng Sima ◽  
Pei Liu ◽  
Lanlan Liu ◽  
Aizhong Mi ◽  
Jianfang Wang
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Color Image ◽  
Matching Pursuit ◽  
Spatial Coherence ◽  
Principal Component ◽  
Distance Constraint ◽  
Sparse Representation Classification ◽  
Simple Linear Iterative Clustering ◽  
Pseudo Color

Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse characteristics of test pixel, which are carried out on the orthogonal matching pursuit (OMP) algorithm. At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. Experiments conducted on two real hyperspectral datasets show the superiority and effectiveness of the proposed method.

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

scholarly journals Gesture recognition based on an improved local sparse representation classification algorithm

2017 ◽  
Vol 22 (S5) ◽  
pp. 10935-10946 ◽  
Author(s):  
Yang He ◽  
Gongfa Li ◽  
Yajie Liao ◽  
Ying Sun ◽  
Jianyi Kong ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Gesture Recognition ◽  
Classification Algorithm ◽  
Sparse Representation Classification ◽  
Local Sparse Representation
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