scholarly journals Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yidong Tang ◽  
Shucai Huang ◽  
Aijun Xue
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Kernel Method ◽  
Matching Pursuit ◽  
Classification Problem ◽  
Kernel Space ◽  
Hypothesis Model ◽  
Extended Surface ◽  
Number Of Classes ◽  
Binary Hypothesis

The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

scholarly journals Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5559
Author(s):  
Na Li ◽  
Ruihao Wang ◽  
Huijie Zhao ◽  
Mingcong Wang ◽  
Kewang Deng ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Matching Pursuit ◽  
Small Sample Size ◽  
Small Sample ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Diverse Density

To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91.

scholarly journals Hyperspectral image spectral-spatial classification via weighted Laplacian smoothing constraint-based sparse representation

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254362
Author(s):  
Eryang Chen ◽  
Ruichun Chang ◽  
Ke Guo ◽  
Fang Miao ◽  
Kaibo Shi ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Matching Pursuit ◽  
Spatial Relationship ◽  
Nearest Neighbors ◽  
Weighting Factor ◽  
Experimental Results ◽  
Spectral Information ◽  
Weighted Laplacian ◽  
Laplacian Smoothing

As a powerful tool in hyperspectral image (HSI) classification, sparse representation has gained much attention in recent years owing to its detailed representation of features. In particular, the results of the joint use of spatial and spectral information has been widely applied to HSI classification. However, dealing with the spatial relationship between pixels is a nontrivial task. This paper proposes a new spatial-spectral combined classification method that considers the boundaries of adjacent features in the HSI. Based on the proposed method, a smoothing-constraint Laplacian vector is constructed, which consists of the interest pixel and its four nearest neighbors through their weighting factor. Then, a novel large-block sparse dictionary is developed for simultaneous orthogonal matching pursuit. Our proposed method can obtain a better accuracy of HSI classification on three real HSI datasets than the existing spectral-spatial HSI classifiers. Finally, the experimental results are presented to verify the effectiveness and superiority of the proposed method.

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.

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