Spatial-Hessian-Feature-Guided Variational Model for Pan-Sharpening

Liu, Pengfei; Xiao, Liang; Zhang, Jun; Naz, Bushra

doi:10.1109/tgrs.2015.2497966

Cited by 40 publications

(16 citation statements)

References 40 publications

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“…Pan-sharpening [40]- [43] is to obtain the HRHS image by fusing the LRHS image with a high-resolution panchromatic image, which mainly includes component substitution (CS), multiresolution analysis (MRA), bayesian-based approaches, unmixing-based methods and so on. Recently, some approaches like CS and MRA for pan-sharpening has been introduced to the LRHS and HRMS image fusion problem.…”

Section: Related Workmentioning

confidence: 99%

A Twice Optimizing Net With Matrix Decomposition for Hyperspectral and Multispectral Image Fusion

Shen

Liu

Xiao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fusing a low-resolution hyperspectral (LRHS) image and a high-resolution multispectral (HRMS) image to generate a high-resolution hyperspectral (HRHS) image has grown a significant and attractive application in remote sensing fields. Recently, the popularization of deep learning has injected more possibilities into the fusion work. However, there still exists a difficulty that is how to make the best of the acquired LRHS and HRMS images. In this paper, we present a twice optimizing net with matrix decomposition to fulfill the fusion task, which can be roughly divided into three stages: pre-optimization, deep prior learning, post-optimization. Specifically, we first transform this fusion problem into a spectral optimization problem and a spatial optimization problem with the help of matrix decomposition. These two optimization problems can be handled sequentially by solving a linear equation respectively and then we can obtain the initial HRHS image by multiplying the two solutions. Next, we establish the mapping between the initial image and the reference image through an end-to-end deep residual network based on local and non-local connectivity. In order to get better performance, we have customized a loss function specifically for the fusion task as well. Finally, we return the predicted result again to the optimization procedure to get the final fusion image. After the evaluation on three simulated datasets and one real dataset, it illustrates that the proposed method outperforms many state-of-the-art ones.

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Section: Related Workmentioning

confidence: 99%

A Twice Optimizing Net With Matrix Decomposition for Hyperspectral and Multispectral Image Fusion

Shen

Liu

Xiao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Pansharpening is conducted for the spatial resolution improvement of multispectral images. Numerous pansharpening algorithms have been developed in recent decades, which can generally be divided into four main categories: (1) component substitution (CS)- [42][43][44], (2) multiresolution analysis (MRA)- [45][46][47], (3) variational optimization (VO)- [48][49][50] and (4) deep learning (DL)-based methods [9,51,52]. Amongst these methods, the CS-and MRA-based methods are the fastest and the most commonly used.…”

Section: Related Workmentioning

confidence: 99%

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Feng

et al. 2019

Remote Sensing

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Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

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“…Another important category is the model-based fusion approach. On the basis of the study about the image formulation model, some researches regard the solution of the fused image as an inverse optimization problem [12][13][14]. Sparse representation (SR) has been recently used in signal processing successfully.…”

Section: Related Workmentioning

confidence: 99%

Pansharpening via Locality-Constrained Sparse Representation

Tang¹,

Zhou²,

Liang³

2017

Procedings of the British Machine Vision Conference 2017

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Recently, sparse representation based approaches have been shown an effective performance for pansharpening. However, these methods imposed 0 or 1 -norm constraints on the sparse coefficients. The local similarity of sparse coefficients was ignored. Motivated by the importance of data locality, in this paper, we propose a locality-constrained sparse representation algorithm for pansharpening, which keeps the data locality during the sparse representation process. The learned dictionary is able to preserve local data structure, resulting in improved data representation. During the sparse coding stage, analytical solutions are provided based on the basis of mathematic deduction. The pansharpening results show that the proposed method is competitive to the other well-known fusion methods.

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Spatial-Hessian-Feature-Guided Variational Model for Pan-Sharpening

Cited by 40 publications

References 40 publications

A Twice Optimizing Net With Matrix Decomposition for Hyperspectral and Multispectral Image Fusion

A Twice Optimizing Net With Matrix Decomposition for Hyperspectral and Multispectral Image Fusion

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Pansharpening via Locality-Constrained Sparse Representation

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