Maximum Likelihood Estimation-Based Joint Sparse Representation for the Classification of Hyperspectral Remote Sensing Images

Peng, Jiangtao; Li, Luoqing; Tang, Yuan Yan

doi:10.1109/tnnls.2018.2874432

Cited by 148 publications

(48 citation statements)

References 47 publications

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“…Based on the weight in (18), the pixels are divided into three classes: (1) "easy" pixels with small loss ( t ≤ λ 2 ) corresponding to weight 1; (2) "complex" pixels with large loss ( t ≥ λ 1 ) corresponding to weight 0; and (3) "moderate" pixels whose loss is between λ 2 and λ 1 . It is clear that the "complex" pixels are excluded from the JSR model.…”

Section: Self-paced Kernel Joint Sparse Representation (Spkjsr)mentioning

confidence: 99%

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Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

Peng

et al. 2019

Remote Sensing

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By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models.

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Section: Self-paced Kernel Joint Sparse Representation (Spkjsr)mentioning

confidence: 99%

“…The joint sparse representation (JSR)-based classification method is a typical integrated spatial-spectral classifier [9][10][11][12][13][14][15][16][17][18]. JSR pursues a joint representation of spatial neighboring pixels in a linear and sparse representation framework.…”

Section: Introductionmentioning

confidence: 99%

Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

Peng

et al. 2019

Remote Sensing

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“…To adaptively reveal the intrinsic structures of data, sparse representation was introduced into graph learning. Sparse representation is to linearly reconstruct a sample with an over-complete dictionary (Peng, Li, and Tang 2019). For the reconstructed coefficients, most of them are zeros and only a few of them are nonzeros which are termed sparse coefficients.…”

Section: Introductionmentioning

confidence: 99%

Review on graph learning for dimensionality reduction of hyperspectral image

Zhang

Luo

2020

Geo-spatial Information Science

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Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction for a hyperspectral image. Firstly, we review the development of graph learning and its application in a hyperspectral image. Then, we mainly discuss several representative graph methods including two manifold learning methods, two sparse graph learning methods, and two hypergraph learning methods. For manifold learning, we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph. For sparse graph, we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph. For hypergraph learning, we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.

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“…Considering that the temperature on both sides of the coastline point should have a step change, the original ideal brightness temperature curve is modeled as a step function (black line in Figure 1a). By assuming that the observed brightness temperature curve is the convolution of the original ideal brightness temperature curve (i.e., a step function) with an unknown kernel function, the step function can be solved according to an p (0 ≤ p < 1) sparse regularization optimization model [18][19][20][21][22], and the step point of the step function is considered the coastline point.…”

Section: Introductionmentioning

confidence: 99%

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

Zhao

Chen

et al. 2019

Remote Sensing

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Known as input in the Numerical Weather Prediction (NWP) models, Microwave Radiation Imager (MWRI) data have been widely distributed to the user community. With the development of remote sensing technology, improving the geolocation accuracy of MWRI data are required and the first step is to estimate the geolocation error accurately. However, the traditional method, such as the coastline inflection method (CIM), usually has the disadvantages of low accuracy and poor anti-noise ability. To overcome these limitations, this paper proposes a novel ℓ p iterative closest point coastline inflection method ( ℓ p -ICP CIM). It assumes that the field of views (FOVs) across the coastline can degenerate into a step function and employs an ℓ p ( 0 ≤ p < 1 ) sparse regularization optimization model to solve the coastline point. After estimating the coastline points, the ICP algorithm is employed to estimate the corresponding relationship between the estimated coastline points and the real coastline. Finally, the geolocation error can be defined as the distance between the estimated coastline point and the corresponding point on the true coastline. Experimental results on simulated and real data sets show the effectiveness of our method over CIM. The accuracy of the geolocation error estimated by ℓ p -ICP CIM is up to 0 . 1 pixel, in more than 90 % of cases. We also show that the distribution of brightness temperature near the coastline is more consistent with the real coastline and the average geolocation error is reduced by 63 % after geolocation error correction.

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Maximum Likelihood Estimation-Based Joint Sparse Representation for the Classification of Hyperspectral Remote Sensing Images

Cited by 148 publications

References 47 publications

Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

Review on graph learning for dimensionality reduction of hyperspectral image

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

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