Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly ...Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.展开更多
This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was f...This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.展开更多
Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Apertu...Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentify-ing noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an aver-age Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estima-tion error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.展开更多
In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a ...In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.展开更多
The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the ba...The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the basis of GF-1 satellite multispectral image preprocessing, vegetation index was extracted by band math. The nitrogen sensitive vegetation index of apple canopy was selected by correlation analysis of nitrogen content in apple canopy. The best inversion model for the nitrogen content of apple canopy was selected by establishing the regression model of univariate and multivariate factors. The nitrogen content of the canopy of apple orchard in the study area was inverted in space. The results showed that the 6 vegetation indices of RVI, NDVI, EVI, VARI, NPCI and NRI were better correlated with nitrogen content in the vegetation index based on GF-1 satellite multispectral imaging. The best inversion model of nitrogen content in apple canopy layer is the multivariate stepwise regression (MSR) model: Nc = 35.74– 41.978^*NPCI-10.78^*NDVI. The R^2 and RMSE of the model was 0.69 and 1.07. The spatial inversion of nitrogen content in apple orchard canopy was obtained. This study provided theoretical basis and technical support for large-area rapid monitoring of regional fruit tree nutrients.展开更多
目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨...目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。展开更多
Mango is a commercial crop on Hainan Island,China,that is cultivated to develop the tropical rural economy.The development of accurate and up-to-date maps of the spatial distribution of mango plantations is necessary ...Mango is a commercial crop on Hainan Island,China,that is cultivated to develop the tropical rural economy.The development of accurate and up-to-date maps of the spatial distribution of mango plantations is necessary for agricultural monitoring and decision management by the local government.Pixel-based and object-oriented image analysis methods for mapping mango plantations were compared using two machine learning algorithms(support vector machine(SVM)and Random Forest(RF))based on Chinese high-resolution Gaofen-1(GF-1)imagery in parts of Hainan Island.To assess the importance of different features on classification accuracy,a combined layer of four original bands,32 gray-level co-occurrence(GLCM)texture indices,and 10 vegetation indices were used as input features.Then five different sets of variables(5,10,20,and 30 input variables and all 46 variables)were classified with the two machine learning algorithms at object-based level.Results of the feature optimization suggested that homogeneity and variance were very important variables for distinguishing mango plantations patches.The object-based classifiers could significantly improve overall accuracy between 2–7%when compared to pixel-based classifiers.When there were 5 and 10 input variables,SVM showed higher classification accuracy than RF,and when the input variables exceeded 20,RF showed better performances.After the accuracy achieved saturation points,there were only slightly classification accuracy improvements along with the numbers of feature increases for both of SVM and RF classifiers.The results indicated that GF-1 imagery can be successfully applied to mango plantation mapping in tropical regions,which would provide a useful framework for accurate tropical agriculture land management.展开更多
In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive ...In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive image.In this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively tested.Drawing upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and speed.Initially,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and diffusion.Then leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all sub-bands.Simulations and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC.展开更多
The brightness reversal of submarine sand waves appearing in the small satellite constellation for environ- ment and disaster monitoring and forecasting （＂HJ- 1A/B＂） CCD sun glitter images can affect the observati...The brightness reversal of submarine sand waves appearing in the small satellite constellation for environ- ment and disaster monitoring and forecasting （＂HJ- 1A/B＂） CCD sun glitter images can affect the observation and depth inversion of sand wave topography. The simulations of the normalized sun glitter radiance on the submarine sand waves confirm that the reversal would happen at a specific sensor viewing angle, defined as the critical angle. The difference between the calculated critical angle position and the reversal position in the image is about 1＇, which is excellent in agreement. Both the simulation and actual image show that sand wave crests would be indistinct at the reversal position, which may cause problems when using these sun glitter images to analyze spatial characteristics and migration of sand waves. When using the sun glitter image to obtain the depth inversion, one should take the advantage of image properties of sand waves and choose the location in between the reversal position and the brightest position. It is also necessary to pay attention to the brightness reversal when using ＂HI-1A/B＂ CCD images to analyze other oceanic features, such as internal waves, oil slicks, eddies, and ship wakes.展开更多
With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,...With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.展开更多
The impacts of the Hong Kong-Zhuhai-Macao Bridge(HKZMB)on suspended sediment content(SSC)were analysed in the Zhujiang River Estuary based on data from HY-1C,which was launched in September 2018 in China,carrying Coas...The impacts of the Hong Kong-Zhuhai-Macao Bridge(HKZMB)on suspended sediment content(SSC)were analysed in the Zhujiang River Estuary based on data from HY-1C,which was launched in September 2018 in China,carrying Coastal Zone Imager(CZI)and Chinese Ocean Color and Temperature Scanner on it.A new SSC inversion model was established based on the relationship between in-situ SSC and the remote sensing reflectance in red and near-infrared bands of CZI image.HY-1C satellite data obtained from October to December 2019 were applied to retrieve SSC in the Zhujiang River Estuary.The results show that SSC around the HKZMB is ranging from 20 mg/L to 95 mg/L.SSC change obviously on two sides of the bridge.During flooding and ebbing period,SSC increases obviously downstream of the bridge.SSC difference between upstream and downstream is ranging from 5 mg/L to 20 mg/L.Currents flowing across the HKZMB,the change trend of SSC in most places upstream and downstream is almost the same that SSC downstream of the bridge is higher than SSC upstream.The tidal currents interact with bridge piers,inducing vortexes downstream,leading the sediment to re-suspend downstream of the bridge piers.Other factors,including seafloor topography and wind,can also contribute to the distribution of SSC in the Zhujiang River Estuary.展开更多
基金The National Key Research and Development Program of China under contract Nos 2016YFC1400901 and 2018YFC1406600the National Natural Science Foundation of China under contract No.40706057+1 种基金the Environmental Protection and Science and Technology Plan Project of Zhejiang Province of China under contract No.2013A021the Research Center for Air Pollution and Health of Zhejiang University
文摘Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.
文摘This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.
基金Under the auspices of National Natural Science Foundation of China(No.42071385)National Science and Technology Major Project of High Resolution Earth Observation System(No.79-Y50-G18-9001-22/23)。
文摘Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentify-ing noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an aver-age Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estima-tion error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.
基金Supported by National Key R&D Program of China(2017YFD0301004)Natural Science Foundation of Shandong Province,China(ZR2016DP04)Key Project of Shandong Provincial Meteorological Bureau(2017sdqxz03)
文摘In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.
基金the National Natural Science Foundation of China(41671346)National Key Research and Development Program of China (2017YFE0122500)+2 种基金the Taishan Scholar Assistance Program from Shandong Provincial GovernmentFunds of Shandong “Double Tops” Program(SYL2017XTTD02)Shandong major scientific and technological innovation project: Research demonstration and extension of orchard irrigation and fertilization in accurate management(2018CXGC0209).
文摘The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the basis of GF-1 satellite multispectral image preprocessing, vegetation index was extracted by band math. The nitrogen sensitive vegetation index of apple canopy was selected by correlation analysis of nitrogen content in apple canopy. The best inversion model for the nitrogen content of apple canopy was selected by establishing the regression model of univariate and multivariate factors. The nitrogen content of the canopy of apple orchard in the study area was inverted in space. The results showed that the 6 vegetation indices of RVI, NDVI, EVI, VARI, NPCI and NRI were better correlated with nitrogen content in the vegetation index based on GF-1 satellite multispectral imaging. The best inversion model of nitrogen content in apple canopy layer is the multivariate stepwise regression (MSR) model: Nc = 35.74– 41.978^*NPCI-10.78^*NDVI. The R^2 and RMSE of the model was 0.69 and 1.07. The spatial inversion of nitrogen content in apple orchard canopy was obtained. This study provided theoretical basis and technical support for large-area rapid monitoring of regional fruit tree nutrients.
文摘目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。
基金This research was funded by the Opening Foundation Program of Land Use Key Laboratory of the Ministry of Natural Resources of China,the Hainan Provincial Key Laboratory of Practical Research on Tropical Crops Information Technology,China(RDZWKFJJ2019001)the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences(1630072020006,1630072017004 and 1630072019001)+2 种基金the Natural Science Foundation of Hainan,China(619MS100)the National Natural Science Foundation of China(31601211)the Demonstration and Pilot Projects for Comprehensive Rural Reform,China(XXSNZG19-02).
文摘Mango is a commercial crop on Hainan Island,China,that is cultivated to develop the tropical rural economy.The development of accurate and up-to-date maps of the spatial distribution of mango plantations is necessary for agricultural monitoring and decision management by the local government.Pixel-based and object-oriented image analysis methods for mapping mango plantations were compared using two machine learning algorithms(support vector machine(SVM)and Random Forest(RF))based on Chinese high-resolution Gaofen-1(GF-1)imagery in parts of Hainan Island.To assess the importance of different features on classification accuracy,a combined layer of four original bands,32 gray-level co-occurrence(GLCM)texture indices,and 10 vegetation indices were used as input features.Then five different sets of variables(5,10,20,and 30 input variables and all 46 variables)were classified with the two machine learning algorithms at object-based level.Results of the feature optimization suggested that homogeneity and variance were very important variables for distinguishing mango plantations patches.The object-based classifiers could significantly improve overall accuracy between 2–7%when compared to pixel-based classifiers.When there were 5 and 10 input variables,SVM showed higher classification accuracy than RF,and when the input variables exceeded 20,RF showed better performances.After the accuracy achieved saturation points,there were only slightly classification accuracy improvements along with the numbers of feature increases for both of SVM and RF classifiers.The results indicated that GF-1 imagery can be successfully applied to mango plantation mapping in tropical regions,which would provide a useful framework for accurate tropical agriculture land management.
基金Project supported by the Key Area Research and Development Program of Guangdong Province,China(Grant No.2022B0701180001)the National Natural Science Foundation of China(Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China(Grant Nos.2019B010140002 and 2020B111110002)the Guangdong–Hong Kong–Macao Joint Innovation Field Project(Grant No.2021A0505080006).
文摘In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive image.In this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively tested.Drawing upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and speed.Initially,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and diffusion.Then leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all sub-bands.Simulations and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC.
基金The Marine Scientific Public Welfare Research Special Foundation under contract No.201105001the Key Laboratory of Ocean Dynamic Processed and Satellite Oceanography under contract No.SOED1006
文摘The brightness reversal of submarine sand waves appearing in the small satellite constellation for environ- ment and disaster monitoring and forecasting （＂HJ- 1A/B＂） CCD sun glitter images can affect the observation and depth inversion of sand wave topography. The simulations of the normalized sun glitter radiance on the submarine sand waves confirm that the reversal would happen at a specific sensor viewing angle, defined as the critical angle. The difference between the calculated critical angle position and the reversal position in the image is about 1＇, which is excellent in agreement. Both the simulation and actual image show that sand wave crests would be indistinct at the reversal position, which may cause problems when using these sun glitter images to analyze spatial characteristics and migration of sand waves. When using the sun glitter image to obtain the depth inversion, one should take the advantage of image properties of sand waves and choose the location in between the reversal position and the brightest position. It is also necessary to pay attention to the brightness reversal when using ＂HI-1A/B＂ CCD images to analyze other oceanic features, such as internal waves, oil slicks, eddies, and ship wakes.
基金Under the auspices of Natural Science Foundation of China(No.42071342,31870713)Beijing Natural Science Foundation Program(No.8182038)Fundamental Research Funds for the Central Universities(No.2015ZCQ-LX-01,2018ZY06)。
文摘With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.
基金The Zhejiang Key Science and Technology Project under contract No.2020C02004the National Key Research and Development Program of China under contract Nos 2017YFA0604901 and 2017YFA0604902+3 种基金the Basic Public Welfare Research Program of Zhejiang Province under contract No.LGF21D010004the National Key Research and Development Program of China under contract No.2016YFC1401605the National Natural Science Foundation of China under contract No.41776183the Curriculum Ideological and Political Teaching Research Project in the Universities of Zhejiang Province(Grouped Ideological and Political Teaching Model Research in the Subject of Marine Remote Sensing)。
文摘The impacts of the Hong Kong-Zhuhai-Macao Bridge(HKZMB)on suspended sediment content(SSC)were analysed in the Zhujiang River Estuary based on data from HY-1C,which was launched in September 2018 in China,carrying Coastal Zone Imager(CZI)and Chinese Ocean Color and Temperature Scanner on it.A new SSC inversion model was established based on the relationship between in-situ SSC and the remote sensing reflectance in red and near-infrared bands of CZI image.HY-1C satellite data obtained from October to December 2019 were applied to retrieve SSC in the Zhujiang River Estuary.The results show that SSC around the HKZMB is ranging from 20 mg/L to 95 mg/L.SSC change obviously on two sides of the bridge.During flooding and ebbing period,SSC increases obviously downstream of the bridge.SSC difference between upstream and downstream is ranging from 5 mg/L to 20 mg/L.Currents flowing across the HKZMB,the change trend of SSC in most places upstream and downstream is almost the same that SSC downstream of the bridge is higher than SSC upstream.The tidal currents interact with bridge piers,inducing vortexes downstream,leading the sediment to re-suspend downstream of the bridge piers.Other factors,including seafloor topography and wind,can also contribute to the distribution of SSC in the Zhujiang River Estuary.
