The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steg...The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.展开更多
遥感影像中不可避免地包含大量混合像元,传统基于约束线性光谱混合模型(constraint linear spectral mixing model,CLSMM)的混合像元分解往往忽略了像元结构复杂度和端元混合比例的影响。本文采用ASD FieldSpec3高密度反射探头,按照不...遥感影像中不可避免地包含大量混合像元,传统基于约束线性光谱混合模型(constraint linear spectral mixing model,CLSMM)的混合像元分解往往忽略了像元结构复杂度和端元混合比例的影响。本文采用ASD FieldSpec3高密度反射探头,按照不同像元结构和端元混合比例设计了4组样本并测量光谱数据。利用CLSMM计算得到混合像元的反射率,根据均方根误差(root mean square error, RMSE)的变化分析混合度指数和斑块密度指数对分解精度的影响建立混合像元分解误差估算模型并验证模型的精度。结果表明,在一定的实验条件下采用CLSMM计算得到样本的光谱反射数据与实际测量数据的光谱特征基本一致;采用CLSMM的混合像元分解误差与混合度指数、斑块密度指数呈显著的正相关随着2个指数的增加RMSE也呈现明显的上升趋势;利用误差估算模型估算样本的RMSE,发现模型估算的RMSE与原始RMSE相比平均相对误差为16.43%。基于CLSMM进行混合像元分解时,考虑模型的适用场景和像元内部差异性的影响将有利于提高混合像元分解的精度。展开更多
Crown fire damage is a mixture of three principal fire-related components:charred material,scorched foliage,and unaltered green canopy.This study estimated the abundance of these physical alterations in two immediate ...Crown fire damage is a mixture of three principal fire-related components:charred material,scorched foliage,and unaltered green canopy.This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts(Portugal and Italy)by applying linear spectral mixture analysis(LSMA)on Sentinel-2 imagery.The tree crowns fire damage was subsequently mapped,integrating fractional abundance information in a random forest(RF)algorithm,comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal.Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification(LMSAderived RMSE<0.1),the F-scores always were≥90%whether generic endmembers or image endmembers derived information was employed.The environmental heterogeneity of the two study areas affected the fire severity gradients,with a prevalence of the charred(PT)(45–46%)and green class(IT)(44–53%).Post-fire temporal monitoring was initialized by applying the proposed strategies,and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event,with a reduced charcoal predominance and an increasing proportion of green components.展开更多
Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA...Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA）, has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index （SPPI） method is used in this study to extract the first endrnember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky fac- torization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tauto- logically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.展开更多
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242in part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.
文摘遥感影像中不可避免地包含大量混合像元,传统基于约束线性光谱混合模型(constraint linear spectral mixing model,CLSMM)的混合像元分解往往忽略了像元结构复杂度和端元混合比例的影响。本文采用ASD FieldSpec3高密度反射探头,按照不同像元结构和端元混合比例设计了4组样本并测量光谱数据。利用CLSMM计算得到混合像元的反射率,根据均方根误差(root mean square error, RMSE)的变化分析混合度指数和斑块密度指数对分解精度的影响建立混合像元分解误差估算模型并验证模型的精度。结果表明,在一定的实验条件下采用CLSMM计算得到样本的光谱反射数据与实际测量数据的光谱特征基本一致;采用CLSMM的混合像元分解误差与混合度指数、斑块密度指数呈显著的正相关随着2个指数的增加RMSE也呈现明显的上升趋势;利用误差估算模型估算样本的RMSE,发现模型估算的RMSE与原始RMSE相比平均相对误差为16.43%。基于CLSMM进行混合像元分解时,考虑模型的适用场景和像元内部差异性的影响将有利于提高混合像元分解的精度。
基金funded by the European Commission and the Regione Calabria with the POR Calabria FESR FSE 2014-2020source[CUP C39B18000070002]Joao M.N.Silva was funded by the Forest Research Centre,a research unit funded by Fundacao para a Ciência e a Tecnologia IP(FCT),Portugal(UIDB/00239/2020)by the project FireCast–Forecasting fire probability and characteristics for a habitable pyro environment,funded by FCT(PCIF/GRF/0204/2017).
文摘Crown fire damage is a mixture of three principal fire-related components:charred material,scorched foliage,and unaltered green canopy.This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts(Portugal and Italy)by applying linear spectral mixture analysis(LSMA)on Sentinel-2 imagery.The tree crowns fire damage was subsequently mapped,integrating fractional abundance information in a random forest(RF)algorithm,comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal.Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification(LMSAderived RMSE<0.1),the F-scores always were≥90%whether generic endmembers or image endmembers derived information was employed.The environmental heterogeneity of the two study areas affected the fire severity gradients,with a prevalence of the charred(PT)(45–46%)and green class(IT)(44–53%).Post-fire temporal monitoring was initialized by applying the proposed strategies,and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event,with a reduced charcoal predominance and an increasing proportion of green components.
基金Project supported by the Zhejiang Provincial Natural Science Foundation of China(Nos.LY13F020044 and LZ14F030004)the National Natural Science Foundation of China(No.61571170)
文摘Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA）, has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index （SPPI） method is used in this study to extract the first endrnember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky fac- torization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tauto- logically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.
