A Modified Homomorphism Filtering Algorithm for Cloud Removal

Wang, Xia; Li, Minmin; Hong-mei, Tang

doi:10.1109/cise.2010.5677195

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…Dark channel prior 1 is a classic method for the thin cloud removal by the statistical analysis of transparent picture database to obtain the prior knowledge of dark channel distribution 7‐9 . Homomorphic filtering 10 is another traditional method for removing thin clouds, which converts the images to the frequency domain by the Fourier transform, and then uses a high‐pass filter to filter the thin clouds. In general, the traditional methods of image processing primarily use the low‐level characteristics of images, leading to limited and sometimes poor model outputs.…”

Section: Related Workmentioning

confidence: 99%

RGAN: Rethinking generative adversarial networks for cloud removal

Ran

Zhang

2021

Int J of Intelligent Sys

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Optical remote sensing imagery is at the core of many Earth observation activities. Many applications take use of the satellite data's regular, consistent, and global‐scale characteristics, such as farmland monitoring, climate change assessment, land‐cover, and land‐use categorization, and catastrophe assessment. Optical remote sensing images, on the other hand, are frequently impacted by clouds during the collection process, resulting in reduced image clarity, which impairs feature assessment and future usage, and heavy cloud blockage renders the surface information below totally useless. In this paper, We propose a soft attention recurrent neural module based on an encoder‐decoder network, which can solve the cloud occlusion problem. We also propose an adaptive padding convolution at the end of the decoder by taking into account the spatial information, which results in better declouding predictions, and our network achieves good results on the RICE1 and RICE2 data sets.

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Section: Related Workmentioning

confidence: 99%

RGAN: Rethinking generative adversarial networks for cloud removal

Ran

Zhang

2021

Int J of Intelligent Sys

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“…suppose that the center point of the image (the clouds that need to be detected) is y and the solar elevation angle is ϑ y . After radiation correction and atmospheric calibration, the pixel values of the ith band of the image's central point y(y bi ) can be obtained through the Equation 7and coefficients in Table 6, as shown in Equation (11).…”

Section: Coefficients Of Formulamentioning

confidence: 99%

“…Clouds impact the application of visible-multispectral remote sensing images. In satellite images, clouds are seen as white due to the fact of scattering light, which can blur remote sensing images and even prevent scientists from observing the surface and cause the images to be completely unusable [8][9][10][11]. At the same time, there are also shadows corresponding to clouds in the image, which block parts of the ground objects and prevent researchers from observing the surface [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images

et al. 2020

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Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images.

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“…In this approach, a modified neighborhood similar pixel interpolator prediction has been used to predict the spectral value of target pixel from its neighboring similar pixels. Xia Wang et al [7] - [8] use a filtering approach for removing cloud contamination within the satellite images. The proposed algorithm models the remote sensing image as radiation component on behalf of cloud noise information and reflection component as object information.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of cloud contaminated information in optical satellite images

Veena¹,

Kumar²

2014

2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD)

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Optical satellite images are the main source of geophysical information which can be used for various land surface studies. But one of the most challenging issue faced by these images are the severe cloud contamination. As far as optical satellite images are of concern, thick clouds will completely obstructs the observation of landscape and uneven illumination caused by thin clouds will further limits the processing of optical satellite images. So a method to eliminate the impact of clouds in optical satellite images is necessary to improve the effectiveness and availability of remote sensing data for various land surface studies. In this paper, a novel methodology for removing the uneven illumination caused by thick and thin clouds is introduced. For this approach, multitemporal cloud contaminated optical satellite images are required with the assumption that in each image cloud cover change significantly over a short duration of time. A cloud detection algorithm in frequency domain is used to detect the cloud contaminated portions in multitemporal optical satellite images. Suitable cloud free patch from reference images for reconstructing cloud contaminated portions in target image are selected based on lower amount of cloud rate and quality assessment technique. Some experimental analysis is conducted on collected Aqua MODIS sensor captured multitemporal satellite images. Experimental result shows that the proposed algorithm can effectively remove majority of cloud contamination within the optical satellite images. Finally quantitative measures are performed to evaluate the feasibility of the methodology.

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A Modified Homomorphism Filtering Algorithm for Cloud Removal

Cited by 6 publications

References 3 publications

RGAN: Rethinking generative adversarial networks for cloud removal

RGAN: Rethinking generative adversarial networks for cloud removal

A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images

Reconstruction of cloud contaminated information in optical satellite images

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