The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland im...The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.展开更多
Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has b...Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.展开更多
This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS trans...This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component,respectively.The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation,and undertaking the inverse IHS transformation.The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image.On the basis of the visual and statistical analysis results of the experimental study,we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion.It has potential advantages in image mapping optimization,object recognition,and weak information sharpening.展开更多
湖泊生态系统中溶解性有机物(dissolved organic matter,DOM)来源复杂,不同污染源输入差异显著,并深刻影响着湖泊物质循环与生态功能。以洞庭湖为研究对象,利用傅里叶变换离子回旋共振质谱(Fourier transform ion cyclotron resonance m...湖泊生态系统中溶解性有机物(dissolved organic matter,DOM)来源复杂,不同污染源输入差异显著,并深刻影响着湖泊物质循环与生态功能。以洞庭湖为研究对象,利用傅里叶变换离子回旋共振质谱(Fourier transform ion cyclotron resonance mass spectrometry,FT-ICR MS)分子表征技术,结合主成分分析(principal component analysis,PCA)-绝对主成分分数(absolute principal component scores,APCS)-多元线性回归(multiple linear regression,MLR)受体模型,定量解析湖区外源DOM的分子特征及贡献。结果表明:湖水DOM以CHO化合物为主,枯水期富含含硫化合物,丰水期含氮化合物比例较高;2个季节的DOM均以高度不饱和类化合物为主,且丰水期DOM的芳香性和稳定性更强;污染源DOM整体不饱和度和芳香性较高,难以降解;受体模型定量结果显示,外源对DOM的贡献顺序为农田水(37.7%)>污水(26.7%)>鱼塘水(18.3%)>未知来源(17.3%)。研究揭示了农业面源污染和生活污水是洞庭湖DOM的主要输入来源,可为湖泊DOM迁移转化机制解析和流域污染治理提供科学依据。展开更多
基金supported by National Natural Science Foundation of China(No.61761027)Gansu Young Doctor’s Fund for Higher Education Institutions(No.2021QB-053)。
文摘The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.
基金The National Science Foundation for Young Scientists of China under contract No.41306193the National Special Research Fund for Non-Profit Marine Sector of China under contract No.201105016the ESA-MOST Dragon 3 Cooperation Programme under contract No.10501
文摘Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.
文摘This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component,respectively.The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation,and undertaking the inverse IHS transformation.The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image.On the basis of the visual and statistical analysis results of the experimental study,we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion.It has potential advantages in image mapping optimization,object recognition,and weak information sharpening.
文摘湖泊生态系统中溶解性有机物(dissolved organic matter,DOM)来源复杂,不同污染源输入差异显著,并深刻影响着湖泊物质循环与生态功能。以洞庭湖为研究对象,利用傅里叶变换离子回旋共振质谱(Fourier transform ion cyclotron resonance mass spectrometry,FT-ICR MS)分子表征技术,结合主成分分析(principal component analysis,PCA)-绝对主成分分数(absolute principal component scores,APCS)-多元线性回归(multiple linear regression,MLR)受体模型,定量解析湖区外源DOM的分子特征及贡献。结果表明:湖水DOM以CHO化合物为主,枯水期富含含硫化合物,丰水期含氮化合物比例较高;2个季节的DOM均以高度不饱和类化合物为主,且丰水期DOM的芳香性和稳定性更强;污染源DOM整体不饱和度和芳香性较高,难以降解;受体模型定量结果显示,外源对DOM的贡献顺序为农田水(37.7%)>污水(26.7%)>鱼塘水(18.3%)>未知来源(17.3%)。研究揭示了农业面源污染和生活污水是洞庭湖DOM的主要输入来源,可为湖泊DOM迁移转化机制解析和流域污染治理提供科学依据。
