Universal reconstruction method for radiometric quality improvement of remote sensing images

Shen, Huanfeng; Liu, Yanfang; Ai, Tinghua; Wang, Yi; Wu, Bo

doi:10.1016/j.jag.2010.04.002

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…At last, the steepest gradient descent method Shen et al, 2010) is employed to solve the fusion images.…”

Section: The Fusion Methodsmentioning

confidence: 99%

Integrated Fusion Method for Multiple Temporal-Spatial-Spectral Images

Shen

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Data fusion techniques have been widely researched and applied in remote sensing field. In this paper, an integrated fusion method for remotely sensed images is presented. Differently from the existed methods, the proposed method has the performance to integrate the complementary information in multiple temporal-spatial-spectral images. In order to represent and process the images in one unified framework, two general image observation models are firstly presented, and then the maximum a posteriori (MAP) framework is used to set up the fusion model. The gradient descent method is employed to solve the fused image. The efficacy of the proposed method is validated using simulated images.

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“…At last, the steepest gradient descent method Shen et al, 2010) is employed to solve the fusion images.…”

Section: The Fusion Methodsmentioning

confidence: 99%

Integrated Fusion Method for Multiple Temporal-Spatial-Spectral Images

Shen

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In the field of digital image processing, the process of restoring the missing data in an image is called image inpainting [1]. Since the emergence of image inpainting problems, several algorithms have been widely studied and applied, such as interpolation methods [2,3], partial differential equation methods [4,5], total variation methods [6][7][8], texture synthesis [9], and the Huber-Markov method [10,11]. All these algorithms were first proposed and applied for two-dimensional images and were mainly applied for the restoration of small areas.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning

Xiaorui

2019

Applied Sciences

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To address the problems of defective pixels and strips in hyperspectral images affecting subsequent processing and applications, we modeled the hyperspectral image (HSI) inpainting problem as a sparse signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning. First, we studied the HSI observation model under the assumption of additive noise. We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an online learning optimization method and performing dictionary learning using a robust function. Afterwards, the image was sparsely encoded by applying the variable decomposition and augmented Lagrangian sparse regression method. Finally, the inpainted HSI was obtained by sparse reconstruction. The experimental results showed that compared with the existing algorithms, the algorithm proposed herein could effectively inpaint the defective HSI under different noise conditions with a shorter calculation time than those of existing methods and other dictionary learning inpainting algorithms.

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“…Recently, many reconstruction methods for remotely sensed images have been proposed, which can be classified into four categories: spatial-based, spectral-based, temporal-based, and hybrid methods. The spatial-based methods take advantage of the relationships between different pixels in the spatial dimension without any other spectral and temporal auxiliary images and include interpolation methods [26], propagated diffusion methods [27], [28], variation-based methods [21], [29]- [33], and exemplar-based methods [34], [35]. This kind of method cannot reconstruct a large missing area because there is not enough reference information.…”

Section: Introductionmentioning

confidence: 99%

Nonlocal Tensor Completion for Multitemporal Remotely Sensed Images’ Inpainting

Yokoya

Zhu

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, non-local correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method.

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Universal reconstruction method for radiometric quality improvement of remote sensing images

Cited by 10 publications

References 35 publications

Integrated Fusion Method for Multiple Temporal-Spatial-Spectral Images

Integrated Fusion Method for Multiple Temporal-Spatial-Spectral Images

Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning

Nonlocal Tensor Completion for Multitemporal Remotely Sensed Images’ Inpainting

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