Hyperspectral and multispectral image fusion using CNMF with minimum endmember simplex volume and abundance sparsity constraints

Zhang, Yifan; Wang, Yakun; Liu, Yang; Zhang, Chuwen; He, Mingyi; Mei, Shaohui

doi:10.1109/igarss.2015.7326172

Cited by 20 publications

(16 citation statements)

References 4 publications

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“…Charis et al proposed an NMF-based fusion method with several physical constraints by jointly unmixing the HIS and MSI data into pure reflectance spectra of the observed materials for hyperspectral super-resolution. A regularized CNMF-based method was proposed [34] by introducing the volume of signature vectors' simplex regularizer, yet the heuristic algorithm did not improve the performance substantially. Lin et al [32] proposed a convex optimization-based CNMF based on the sum-of-squared distances (SSD) between all the endmembers, in which the sparsity and SSD-based regularizers were employed to bring significant improvements in fusion performance.…”

Section: Introductionmentioning

confidence: 99%

Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution

Yang

Ping

et al. 2019

Applied Sciences

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The limitations of hyperspectral sensors usually lead to coarse spatial resolution of acquired images. A well-known fusion method called coupled non-negative matrix factorization (CNMF) often amounts to an ill-posed inverse problem with poor anti-noise performance. Moreover, from the perspective of matrix decomposition, the matrixing of remotely-sensed cubic data results in the loss of data’s structural information, which causes the performance degradation of reconstructed images. In addition to three-dimensional tensor-based fusion methods, Craig’s minimum-volume belief in hyperspectral unmixing can also be utilized to restore the data structure information for hyperspectral image super-resolution. To address the above difficulties simultaneously, this article incorporates the regularization of joint spatial-spectral smoothing in a minimum-volume simplex, and spatial sparsity—into the original CNMF, to redefine a bi-convex problem. After the convexification of the regularizers, the alternating optimization is utilized to decouple the regularized problem into two convex subproblems, which are then reformulated by separately vectorizing the variables via vector-matrix operators. The alternating direction method of multipliers is employed to split the variables and yield the closed-form solutions. In addition, in order to solve the bottleneck of high computational burden, especially when the size of the problem is large, complexity reduction is conducted to simplify the solutions with constructed matrices and tensor operators. Experimental results illustrate that the proposed algorithm outperforms state-of-the-art fusion methods, which verifies the validity of the new fusion approach in this article.

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Section: Introductionmentioning

confidence: 99%

Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution

Yang

Ping

et al. 2019

Applied Sciences

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“…In recent years, the matrix decomposition-based methods and Bayesian-based methods are proposed. The coupled non-negative matrix factorization (CNMF) [1,23] and non-negative sparse coding (NNSC) are typical algorithms of matrix decomposition. The fused image is generated by a non-negative matrix factorization (NMF) [24] model to realize the fusion of HS and PAN images under some constraints to estimate endmember and abundance matrices.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Zhong

Lei

et al. 2019

Remote Sensing

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Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.

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“…By performing this kind of image fusion, more use is made of the available data, and this can be useful for many applications such as classification [3], target detection, snow cover analysis [4], etc. In recent years, more fusion scenarios are becoming possible, such as the fusion of hyperspectral (HS) images and PAN images, referred to as hypersharpening [5][6][7][8][9][10] to yield HS images of high spatial resolution and the fusion of MS and HS images [11][12][13][14][15][16][17][18][19][20][21][22] to yield high spatial resolution HS images. Both MS/HS fusion and hypersharpening can be seen as extensions of the pansharpening problem, where the source images have more bands.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 Image Fusion Using a Deep Residual Network

2018

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Single sensor fusion is the fusion of two or more spectrally disjoint reflectance bands that have different spatial resolution and have been acquired by the same sensor. An example is Sentinel-2, a constellation of two satellites, which can acquire multispectral bands of 10 m, 20 m and 60 m resolution for visible, near infrared (NIR) and shortwave infrared (SWIR). In this paper, we present a method to fuse the fine and coarse spatial resolution bands to obtain finer spatial resolution versions of the coarse bands. It is based on a deep convolutional neural network which has a residual design that models the fusion problem. The residual architecture helps the network to converge faster and allows for deeper networks by relieving the network of having to learn the coarse spatial resolution part of the inputs, enabling it to focus on constructing the missing fine spatial details. Using several real Sentinel-2 datasets, we study the effects of the most important hyperparameters on the quantitative quality of the fused image, compare the method to several state-of-the-art methods and demonstrate that it outperforms the comparison methods in experiments.

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Hyperspectral and multispectral image fusion using CNMF with minimum endmember simplex volume and abundance sparsity constraints

Cited by 20 publications

References 4 publications

Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution

Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Sentinel-2 Image Fusion Using a Deep Residual Network

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