A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

Maalouf, Aldo; Carré, Philippe; Augereau, Bertrand; Fernández-Maloigne, Christine

doi:10.1109/tgrs.2008.2010454

Cited by 136 publications

(39 citation statements)

References 15 publications

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“…85 for regenerating a contaminated RS image by cloud. This technique is spatially based, which means that we rely only on the spatial correlation of the corrupted data in SPOT, Landsat, BDOrtho images.…”

Section: Inpaintingmentioning

confidence: 99%

Review of multiscale geometric decompositions in a remote sensing context

Zaouali¹,

Bouzidi²,

Zagrouba³

2016

J. Electron. Imaging

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Abstract. The quest for optimal representations is considered a challenging goal in the field of image processing. This consists of reducing the processing's complexity while ensuring an efficient reconstruction. An optimal representation should conserve the properties of the image pertaining to smooth content and contours. The multiscale geometric decompositions (MGD) were designed to reach this finality. They were used in many fields and for different purposes, such as feature extraction, detail enhancing, and change detection. A state-of-art of these decompositions is proposed in this paper. We present their theoretical definitions and how they capture the feature of the objects within an image. An overview table is elaborated where we summarize the methods, the data and the different criteria of assessment used in the studied cases. We are interested, particularly, in the use of MGD in a remote sensing (RS) context. Thus, some examples of their applications on RS images are studied. A discussion is presented based on the analyzed cases.

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Section: Inpaintingmentioning

confidence: 99%

Review of multiscale geometric decompositions in a remote sensing context

Zaouali¹,

Bouzidi²,

Zagrouba³

2016

J. Electron. Imaging

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“…This method aims to remove large objects, fill gaps, undefined or damaged regions of an image, in order to restore or make it more visible [2,12]. In this paper, we consider as damaged regions to be treated, areas of dense clouds, as done in [14], and shadow regions. The Inpainting method uses the technique of nearest neighbor interpolation and the effect created is that the nearest pixel appears larger, because its value is assigned to an undefined pixel [9].…”

Section: Inpaintingmentioning

confidence: 99%

“…The removal of dense clouds and their shadows are basically divided into two approaches, the first relates to the use of a non-cloud cover reference image in a multi-temporal analysis [18], or even doing an interpolation with contribution of a radar image (SAR) [8]. The other approach attempts to estimate what is being covered by cloud and/or shadow in image, using the same or similar approaches to the inpainting method [2,12,14].…”

Section: Introductionmentioning

confidence: 99%

The Use of High-Pass Filters and the Inpainting Method to Clouds Removal and Their Impact on Satellite Images Classification

Siravenha

Sousa

Bispo

et al. 2011

Lecture Notes in Computer Science

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Abstract. This paper proposes a new technique to smooth undesirable elements of the atmosphere, such as fogs, clouds and shadows, which damage and lead to loss of image data. In our approach, an efficient way to detect clouds and shadows is presented. The method applies constants related to such undesirable elements, as well as a High boost Filter in the homomorphic filtering for scattered clouds removal. We highlight the use of the Inpainting method, which replaces contaminated pixels using a nearest neighbor interpolation. Beside this, the proposed algorithm adopts a morphologic opening of the image that aims to suppress some isolated occurrences in the scene. The results are evaluated by Kappa coefficient and PSNR index, proving the good performance of the method.

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“…Inpainting family of methods uses the cloud free part of the image to reconstruct the contaminated part. Maalouf et al [4] used the continuity of geometric flow of the image to synthesize the geometry inside the cloudy part. Bandlet transform followed by multiscale grouping is employed to learn the geometrical structure of the clear portion of the image.…”

Section: Introductionmentioning

confidence: 99%

Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

Adam

Mönks

Esch

et al. 2018

The 2nd International Electronic Conference on Remote Sensing

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Clouds and cloud-shadow are a persistent problem in all optical satellite imagery. Plenty of methods have been suggested in the literature to address this problem, and reconstruct the missing part of the optical signal. In this work, three methods representative of different approaches to the cloud removal problem are compared. The first method is temporal fitting using Fourier series, which benefits from the temporal continuity of the signal. The second method uses sparse spectral unmixing to fill in the missing areas. The third method employs radiometric consistency as a tool to determine the missing part of the signal. These three methods are first presented and their theoretical background described, followed by a discussion of their implied assumptions and general performance. A set of experiments using Landsat 8 time series with diverse land cover types were conducted. The quantitative results of the three methods using simulated clouds are presented. Finally, some concluding remarks about the relative advantages of the three approaches are listed, in addition to some recommendations about their use.

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A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

Cited by 136 publications

References 15 publications

Review of multiscale geometric decompositions in a remote sensing context

Review of multiscale geometric decompositions in a remote sensing context

The Use of High-Pass Filters and the Inpainting Method to Clouds Removal and Their Impact on Satellite Images Classification

Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

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