A Thin Cloud Removal Method from Remote Sensing Image for Water Body Identification

In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.

Cloud removal of remote sensing image based on multi-output support vector regression

Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.

Attribute-Based Access Control Scheme with Efficient Revocation in Cloud Computing

Attribute-based encryption(ABE) supports the fine-grained sharing of encrypted data.In some common designs,attributes are managed by an attribute authority that is supposed to be fully trustworthy.This concept implies that the attribute authority can access all encrypted data,which is known as the key escrow problem.In addition,because all access privileges are defined over a single attribute universe and attributes are shared among multiple data users,the revocation of users is inefficient for the existing ABE scheme.In this paper,we propose a novel scheme that solves the key escrow problem and supports efficient user revocation.First,an access controller is introduced into the existing scheme,and then,secret keys are generated corporately by the attribute authority and access controller.Second,an efficient user revocation mechanism is achieved using a version key that supports forward and backward security.The analysis proves that our scheme is secure and efficient in user authorization and revocation.

Algorithm Development of Cloud Removal from Solar Images Based on Pix2Pix Network

Sky clouds affect solar observations significantly.Their shadows obscure the details of solar features in observed images.Cloud-covered solar images are difficult to be used for further research without pre-processing.In this paper,the solar image cloud removing problem is converted to an image-to-image translation problem,with a used algorithm of the Pixel to Pixel Network(Pix2Pix),which generates a cloudless solar image without relying on the physical scattering model.Pix2Pix is consists of a generator and a discriminator.The generator is a well-designed U-Net.The discriminator uses PatchGAN structure to improve the details of the generated solar image,which guides the generator to create a pseudo realistic solar image.The image generation model and the training process are optimized,and the generator is jointly trained with the discriminator.So the generation model which can stably generate cloudless solar image is obtained.Extensive experiment results on Huairou Solar Observing Station,National Astronomical Observatories,and Chinese Academy of Sciences(HSOS,NAOC and CAS)datasets show that Pix2Pix is superior to the traditional methods based on physical prior knowledge in peak signal-to-noise ratio,structural similarity,perceptual index,and subjective visual effect.The result of the PSNR,SSIM and PI are 27.2121 dB,0.8601 and 3.3341.

Spatial-temporal variability of snow cover over the Amur River Basin inferred from MODIS daily snow products in recent decades

MODIS snow products MOD10A1\MYD10A1 provided us a unique chance to investigate snow cover as well as its spatial-temporal variability in response to global changes from regional and global perspectives.By means of MODIS snow products MOD10A1\MYD10A1 derived from an extensive area of the Amur River Basin,mainly located in the Northeast part of China,some part in far east area of the former USSR and a minor part in Republic of Mongolia,the reproduced snow datasets after removal of cloud effects covering the whole watershed of the Amur River Basin were generated by using 6 different cloud-effect-removing algorithms.The accuracy of the reproduced snow products was evaluated with the time series of snow depth data observed from 2002 to 2010 within the Chinese part of the basin,and the results suggested that the accuracies for the reproduced monthly mean snow depth datasets derived from 6 different cloud-effect-removing algorithms varied from 82%to 96%,the snow classification accuracies(the harmonic mean of Recall and Precision)was higher than 80%,close to the accuracy of the original snow product under clear sky conditions when snow cover was stably accumulated.By using the reproduced snow product dataset with the best validated cloud-effect-removing algorithm newly proposed,spatial-temporal variability of snow coverage fraction(SCF),the date when snow cover started to accumulate(SCS)as well as the date when being melted off(SCM)in the Amur River Basin from 2002 to 2016 were investigated.The results indicated that the SCF characterized the significant spatial heterogeneity tended to be higher towards East and North but lower toward West and South over the Amur River Basin.The inter-annual variations of SCF showed an insignificant increase in general with slight fluctuations in majority part of the basin.Both SCS and SCM tended to be slightly linear varied and the inter-annual differences were obvious.In addition,a clear decreasing trend in snow cover is observed in the region.Trend analysis(at 10%significance level)showed that 71%of areas between 2,000 and 2,380 m a.s.l.experienced a reduction in duration and coverage of annual snow cover.Moreover,a severe snow cover reduction during recent years with sharp fluctuations was investigated.Overall spatial-temporal variability of Both SCS and SCM tended to coincide with that of SCF over the basin in general.

Accuracy assessment of four cloud-free snow cover products over the Qinghai-Tibetan Plateau

Four up-to-date daily cloud-free snow products–IMS(InteractiveMultisensor Snow products),MOD-SSM/I(combination of the MODIS andSSM/I snow products),MOD-B(Blending method basing on the MODISsnow cover products)and TAI(Terra–Aqua–IMS)–with high-resolutionsover the Qinghai-Tibetan Plateau(QTP)were comprehensively assessed.Comparisons of the IMS,MOD-SSM/I,MOD-B and TAI cloud-free snowproducts against meteorological stations observations over 10 snowseasons(2004–2013)over the QTP indicated overall accuracies of 76.0%,89.3%,92.0%and 92.0%,respectively.The Khat values of the IMS,MODSSM/I,MOD-B and TAI products were 0.084,0.463,0.428 and 0.526,respectively.The TAI products appear to have the best cloud-removalability among the four snow products over the QTP.Based on theassessment,an I-TAI(Improvement of Terra–Aqua–IMS)snow productwas proposed,which can improve the accuracy to some extent.However,the algorithms of the MODIS series products show instabilitywhen identifying wet snow and snow under forest cover over the QTP.The snow misclassification is an important limitation of MODIS snowcover products and requires additional improvements.