Cloud Removal From Multitemporal Satellite Images Using Information Cloning

Lin, Chao-Hung; Tsai, Pei‐Hsuan; Lai, Kang-Hua; Chen, Jyun-Yuan

doi:10.1109/tgrs.2012.2197682

Cited by 212 publications

(104 citation statements)

References 20 publications

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“…The information cloning method developed by Lin et al [11] addresses the cloud recovery as a global optimization problem. It utilizes a temporal correlation of multitemporal images, and clones patches from cloud-free images which match well with radiometric conditions of the reference image.…”

Section: Information Cloningmentioning

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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Section: Information Cloningmentioning

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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“…The idea of the adjustment parameter learning comes from the Poisson equation-based image recovery. Lin et al [32] proposed a cloud cloning algorithm in their paper. The basic idea is to clone information from cloud-free patches to their corresponding cloud-contaminated patches in several images (These images are obtained from different time, but for the same place).…”

Section: Adjustment Of the Predicted Learning Interpolation Pixelsmentioning

confidence: 99%

Learning Interpolation via Regional Map for Pan-Sharpening

Cheng

Liu

Li³

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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Although the bandwidth of the high-resolution panchromatic (HR PAN) image is wide, it is narrow in each band of the low-resolution multispectral (LR MS) image. Hence, the spatial resolution of the HR PAN image is much higher than that of the LR MS image. However, HR PAN image only has a single band. The purpose of the Pan-sharpening algorithm is to make the Pan-sharpened image with both high spatial resolution and good spectral information. In this paper, a novel learning interpolation method for Pan-sharpening is proposed by expanding the sketch information in the HR PAN image. The sketch information contains the edges and lines features of the image, and each segment of the sketch information has its own direction. According to the primal sketch graph of the HR PAN image, a regional map is obtained by a designed geometrical template. Since the size of the HR PAN image is different from that of the LR MS image, the LR MS image is interpolated into an interpolated multispectral (IMS) image by the nearest interpolation method. In addition, the IMS image can be mapped into the structure and the nonstructure regions by this regional map. The nonstructure regions are divided into the smooth and the texture regions by a variance value. For the structure and texture regions, the interpolated pixels in the IMS image are relearned and readjusted by the proposed structure and texture learning interpolation method, respectively. Experimental results show that the proposed Pan-sharpening method can provide superior performance in both visual effect and quality metrics, particularly for the images with a large spectral difference.

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“…Spatial reconstruction has been described by removing cloud-contaminated portions in satellite images and cloning information from cloud-free patches [11]. Reconstruction of cloudy areas has also been proposed based on compressive sensing (CS) theory in underdetermined linear equation systems [12].…”

Section: Introductionmentioning

confidence: 99%

“…Reconstruction of cloudy areas has also been proposed based on compressive sensing (CS) theory in underdetermined linear equation systems [12]. For example, a recently developed method uses two multi-temporal dictionary learning methods based on the expanded K-means clustering process and Bayesian algorithms [11]. Furthermore, at the acquisition layer, the commonly used Landsat NDVI data products are multi-day Maximum Value Composite (MVC) [13]; however, some noise remains in the final imagery.…”

Section: Introductionmentioning

confidence: 99%

Optical Cloud Pixel Recovery via Machine Learning

et al. 2017

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Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Studies that are heavily dependent on optical sensors are subject to data loss due to cloud coverage. Specifically, cloud contamination is a hindrance to long-term environmental assessment when using information from satellite imagery retrieved from visible and infrared spectral ranges. Landsat has an ongoing high-resolution NDVI record starting from 1984. Unfortunately, this long time series NDVI data suffers from the cloud contamination issue. Though both simple and complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques have limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum using a Random Forest (RF) trained and tested with multi-parameter hydrologic data. The RF-based OCPR model is compared with a linear regression model to demonstrate the capability of OCPR. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance. The RF-based OCPR method achieves a root mean squared error of 0.016 between predicted and observed NDVI reflectance values. The linear regression model achieves a root mean squared error of 0.126. Our findings suggest that the RF-based OCPR method is effective to repair cloudy pixels and provides continuous and quantitatively reliable imagery for long-term environmental analysis.

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Cloud Removal From Multitemporal Satellite Images Using Information Cloning

Cited by 212 publications

References 20 publications

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

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

Learning Interpolation via Regional Map for Pan-Sharpening

Optical Cloud Pixel Recovery via Machine Learning

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