Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Feng, Ruyi; Zhong, Yanfei; Wang, Lizhe; Wang, Lin

doi:10.3390/rs9121218

Cited by 15 publications

(4 citation statements)

References 49 publications

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“…Some new methods based on deep learning have provided new insights into the denoising problem. The deep convolutional neural network not only have superior effects on image classification [54], [55], but also shows promising performances in denoising. Unlike traditional methods, it has the ability to learn features, and many deep learning-based denoising methods achieve higher (peak signal-to-noise ratio) PSNR than traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Denoising With Dual Deep CNN

Shan

Liu

et al. 2019

IEEE Access

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A new hyperspectral image denoising algorithm, called the dual deep convolutional neural network (DD-CNN), is proposed in this paper. In contrast to internal denoising methods that utilize only the features from the target noisy image, the DD-CNN extensively explores the similarities between the target noisy image and the clean reference image from other bands. As external data, the reference images are selected based on the structural similarity index metric (SSIM). The DD-CNN is composed of two CNNs: one is responsible for extracting the features of the target image, and the other is responsible for extracting features from the reference image. A new activation function is proposed that activates the two types of features in the DD-CNN. Based on the dual structure and the new activation function, the external features extracted from the reference images are thoroughly integrated into the internal features of the target noise image. We experimented on different datasets with different noise levels; we also tested special cases for reference images with extra or undesirable features. The DD-CNN algorithm can effectively utilize the similarity between the external image and the target image. When the noise level is high, the advantages of the DD-CNN are obvious.INDEX TERMS Hyperspectral image denoising, deep dual neural network, feature learning, activation function.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Denoising With Dual Deep CNN

Shan

Liu

et al. 2019

IEEE Access

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“…Each pixel can be formulated as a linear combination of endmembers with additional noise. The idea is that each incoming light ray interacts with only one material before reaching the sensor [18,19]. However, the LMM is invalid when there are intimate mixtures, terrain relief, or volumetric scattering [20].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Liu

Cheng

et al. 2018

Remote Sensing

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Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.Keywords: additive white Gaussian noise (AWGN); hyperspectral images (HSIs); mixed noise; bandwise generalized bilinear model (BGBM); alternative direction method of multipliers (ADMM) some flexible models based on signal processing, such as post-nonlinear model [22], neural network model [23] and kernel model [24]. The second category includes some physical based models, such as intimate mixture model [25], bilinear mixture model (BMM) [26][27][28][29][30][31][32][33] and multilinear mixing model [34][35][36]. Among them, the BMM only takes the second-order scattering into consideration, while the higher-order interactions of light are ignored [18]. The reason is that the interactions of orders larger than two, not only have minor contribution for improving the unmixing accuracy than that of second-order scattering, but also bring in tremendous computational costs [21]. Several representative models known as the family of BMM have been proposed. The Nascimento model (NM) [26] is an extended LMM with additional virtual endmembers, the Fan's model (FM) is the truncated Taylor expansion of nonlinear mixing function [27], and the GBM [28] can be seen as the generalization of LMM and FM, which can efficiently deal with the assumptions in BMM. Different methods have been proposed for GBM unmixing of HSI, Halimi et al. developed a Bayesian algorithm to estimate the abundance and noise variance of the GBM [28]. Besides, they also proposed a pixel-wise unmixing method based on the gradient descent algorithm (GDA) [29]. Moreover, Yokoya et al. proposed the semi-nonnegative matrix factorization (semi-NMF) as a new optimization method for GBM based HSI unmixing [30]. Furthermore, Li et al. developed the bound projected optimal gradient method (BPOGM) for GBM unmixng, and it can achieve the optimal convergence rate of O( 1 k 2 ), where k denotes the number of iteration in BPOGM [31].Most unmixing methods based on the GBM are implicitly developed for Gaussian noise, and the underlying assumptio...

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“…However, if the spatial resolution of the sensor is low, different materials may jointly occupy a single pixel [14]. The resulting spectral measurement will be a mixed pixel, composed of the individual pure spectra (i.e., endmembers) and their corresponding fractional abundances [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Much research has been under taken on remotely sensed hyperspectral data processing in recent years, covering topics such as dimensionality reduction [1][2][3], classification [4,5], target detection [6][7][8], data compression [9,10], and spectral unmixing [11][12][13][14][15][16][17][18][19]. Most of this research assumes that each pixel vector comprises the response of a single underlying material in the scene.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Sparse Pruning Endmember Extraction Algorithm with a Combined Minimum Volume and Deviation Constraint

Jun

Yu³

2018

Remote Sensing

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Abstract:In this paper, an automatic sparse pruning endmember extraction algorithm with a combined minimum volume and deviation constraint (SPEEVD) is proposed. The proposed algorithm can adaptively determine the number of endmembers through a sparse pruning method and, at the same time, can weaken the noise interference by a minimum volume and deviation constraint. A non-negative matrix factorization solution based on the projection gradient is mathematically applied to solve the combined constrained optimization problem, which makes sure that the convergence is steady and robust. Experiments were carried out on both simulated data sets and real AVIRIS data sets. The experimental results indicate that the proposed method does not require a predetermined endmember number, but it still manifests an improvement in both the root-mean-square error (RMSE) and the endmember spectra, compared to the other state-of-the-art methods, most of which need an accurate pre-estimation of endmember number.

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Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Cited by 15 publications

References 49 publications

Hyperspectral Image Denoising With Dual Deep CNN

Hyperspectral Image Denoising With Dual Deep CNN

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

An Automatic Sparse Pruning Endmember Extraction Algorithm with a Combined Minimum Volume and Deviation Constraint

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