The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield estimation.In this study,the OTSU algorith...The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield estimation.In this study,the OTSU algorithm and Mean-Shift algorithm were employed to automatically determine threshold values for mapping two main rotated crop patterns at the pixel scale.A time series analysis was conducted to extract the spatial distribution of rice-wheat and wheat-maize rotations in the Chuanhang irrigation district from 2016 to 2020.The results demonstrate that both threshold segmentation algorithms are reliable in extracting the spatial distribution of the crops,with an overall accuracy exceeding 80%.Additionally,both Kappa coefficients surpass 0.7,indicating better performance by OTSU method.Over the period from 2016 to 2020,the area occupied by rice-wheat rotation cropping ranged from 12500 to 14400 hm 2;whereas wheat-maize rotation cropping exhibited smaller and more variable areas ranging from 19730 to 34070 hm 2.These findings highlight how remote sensing-based approaches can provide reliable support for rapidly and accurately identifying the spatial distribution of main crop rotation patterns within a large irrigation district.展开更多
In the era of big data,outsourcing massive data to a remote cloud server is a promising approach.Outsourcing storage and computation services can reduce storage costs and computational burdens.However,public cloud sto...In the era of big data,outsourcing massive data to a remote cloud server is a promising approach.Outsourcing storage and computation services can reduce storage costs and computational burdens.However,public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users.Privacy-preserving feature extraction techniques are an effective solution to this issue.Because the Rotation Invariant Local Binary Pattern(RILBP)has been widely used in various image processing fields,we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper(called PPRILBP).To protect image content,original images are encrypted using block scrambling,pixel circular shift,and pixel diffusion when uploaded to the cloud server.It is proved that RILBP features remain unchanged before and after encryption.Moreover,the server can directly extract RILBP features from encrypted images.Analyses and experiments confirm that the proposed scheme is secure and effective,and outperforms previous secure LBP feature computing methods.展开更多
基金Jiangsu Water Science and Technology Project(2021081)。
文摘The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield estimation.In this study,the OTSU algorithm and Mean-Shift algorithm were employed to automatically determine threshold values for mapping two main rotated crop patterns at the pixel scale.A time series analysis was conducted to extract the spatial distribution of rice-wheat and wheat-maize rotations in the Chuanhang irrigation district from 2016 to 2020.The results demonstrate that both threshold segmentation algorithms are reliable in extracting the spatial distribution of the crops,with an overall accuracy exceeding 80%.Additionally,both Kappa coefficients surpass 0.7,indicating better performance by OTSU method.Over the period from 2016 to 2020,the area occupied by rice-wheat rotation cropping ranged from 12500 to 14400 hm 2;whereas wheat-maize rotation cropping exhibited smaller and more variable areas ranging from 19730 to 34070 hm 2.These findings highlight how remote sensing-based approaches can provide reliable support for rapidly and accurately identifying the spatial distribution of main crop rotation patterns within a large irrigation district.
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.61872408the Natural Science Foundation of Hunan Province(2020JJ4238)+1 种基金the Social Science Fund of Hunan Province under Grant No.16YBA102the Research Fund of Hunan Provincial Key Laboratory of informationization technology for basic education under Grant No.2015TP1017.
文摘In the era of big data,outsourcing massive data to a remote cloud server is a promising approach.Outsourcing storage and computation services can reduce storage costs and computational burdens.However,public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users.Privacy-preserving feature extraction techniques are an effective solution to this issue.Because the Rotation Invariant Local Binary Pattern(RILBP)has been widely used in various image processing fields,we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper(called PPRILBP).To protect image content,original images are encrypted using block scrambling,pixel circular shift,and pixel diffusion when uploaded to the cloud server.It is proved that RILBP features remain unchanged before and after encryption.Moreover,the server can directly extract RILBP features from encrypted images.Analyses and experiments confirm that the proposed scheme is secure and effective,and outperforms previous secure LBP feature computing methods.
