Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information...Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension.The classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral features.For this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral bands.The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel.The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system.In this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in conjunction.The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI.In the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information.The resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost.The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF.The classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa factor.The proposed scheme achieved greater than 90%results for all the performance evaluation metrics.The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range.The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.展开更多
We have examined ten human subjects with a previously developed instrument for near-infrared diffuse spectral imaging of the female breast.The instrument is based on a tandem,planar scan of two collinear optical fiber...We have examined ten human subjects with a previously developed instrument for near-infrared diffuse spectral imaging of the female breast.The instrument is based on a tandem,planar scan of two collinear optical fibers(one for illumination and one for collection)to image a gently compressed breast in a transmission geometry.The optical data collection features a spatial sampling of 25 points/cm2 over the whole breast,and a spectral sampling of 2 points/nm in the 650-900nm wavelength range.Of the ten human subjects examined,eight are healthy subjects and two are cancer patients with unilateral invasive ductal carcinoma and ductal carcinoma in situ,respectively.For each subject,we generate second-derivative images that identify a network of highly absorbing structures in the breast that we assign to blood vessels.A previously developed paired-wavelength spectral method assigns oxygenation values to the absorbing structures displayed in the second-derivative images.The resulting oxygenation images feature average values over the whole breast that are significantly lower in cancerous breasts(69±14%,n=2)than in healthy breasts(85±7%,n=18)(p<0.01).Furthermore,in the two patients with breast cancer,the average oxygenation values in the cancerous regions are also significantly lower than in the remainder of the breast(invasive ductal carcinoma:49±11%vs 61±16%,p<0.01;ductal carcinoma in situ:58±8%vs 77±11%,p<0.001).展开更多
Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument th...Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.展开更多
针对高光谱遥感图像复杂农作物分类问题,提出了一种基于空谱融合和随机多图的高光谱遥感图像农作物分类方法。通过使用一种潜在特征融合和最优聚类(Latent Features Fusion and Optimal Clustering Framework,LFFOCF)的波段选择方法和...针对高光谱遥感图像复杂农作物分类问题,提出了一种基于空谱融合和随机多图的高光谱遥感图像农作物分类方法。通过使用一种潜在特征融合和最优聚类(Latent Features Fusion and Optimal Clustering Framework,LFFOCF)的波段选择方法和分段主成分分析(Segmented Principal Component Analysis,SPCA)进行光谱降维,采用多尺度二维奇异谱分析(2-D-Singular Spectrum Analysis,2-D-SSA)应用于降维图像,以提取不同尺度的空间特征。将多尺度空间特征与主成分分析(Principal Component Analysis,PCA)得到的全局光谱特征融合送到随机多图(Random Multi-Graphs,RMG)中进行分类。在印度松树、萨利纳斯和龙口数据集上,所提出的方法与一些现有的方法进行了对比实验。实验结果表明,该方法的类别精度(Class Accuracy,CA)、总体分类精度(Overall Accuracy,OA)、平均分类精度(Average Accuracy,AA)和Kappa系数优于这些方法。展开更多
Over recent decades, the global demand for food has continued to grow, owing to population growth and the loss of arable land. Rice ratooning offers new opportunities for increasing rice production and has received re...Over recent decades, the global demand for food has continued to grow, owing to population growth and the loss of arable land. Rice ratooning offers new opportunities for increasing rice production and has received renewed interest because of the minimal additional labor input required for its adoption. Regular, regional-scale monitoring of the spatial patterns of both traditional and ratoon rice cropping systems provides essential information for agricultural resource management and food security studies. However, the similar phenological characteristics of traditional double rice and ratoon rice cropping systems make it challenging to accurately classify these cropping practices based on satellite observations alone. In this study, we first proposed an improved phenology-based rice cropping area detection algorithm using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) imagery. A new index, ratoon rice index, was then developed to automatically delineate ratoon rice cropping areas with the aid of a base map of rice in Hubei Province, China. The accuracy assessment using ground truth data showed that our approach could map both traditional and ratoon rice cropping areas with high user accuracy (91.25% and 91.43%, respectively). The MODIS-retrieved rice cropping areas were validated using annual agricultural census data, and coefficient of determination (R2) values of 0.60 and 0.41 were recorded for traditional and ratoon rice cropping systems, respectively. The total area of ratoon rice was estimated to be 1 283.6 km2, 5.0% of the total rice cropping area, in Hubei Province in 2016. These demonstrated the feasibility of extracting the spatial patterns of both traditional and ratoon rice cropping systems solely from time-series NDVI and field survey data and strides made in facilitating the timely and routine monitoring of traditional and ratoon rice distribution at subnational level. Given sufficient historical satellite and phenology records, the proposed algorithm had the potential to enhance rice cropping area mapping efforts across a broad temporal scale (e.g., from the 1980s to the present).展开更多
文摘Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension.The classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral features.For this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral bands.The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel.The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system.In this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in conjunction.The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI.In the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information.The resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost.The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF.The classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa factor.The proposed scheme achieved greater than 90%results for all the performance evaluation metrics.The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range.The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.
基金supported by the National Institutes of Health,Grant CA95885.
文摘We have examined ten human subjects with a previously developed instrument for near-infrared diffuse spectral imaging of the female breast.The instrument is based on a tandem,planar scan of two collinear optical fibers(one for illumination and one for collection)to image a gently compressed breast in a transmission geometry.The optical data collection features a spatial sampling of 25 points/cm2 over the whole breast,and a spectral sampling of 2 points/nm in the 650-900nm wavelength range.Of the ten human subjects examined,eight are healthy subjects and two are cancer patients with unilateral invasive ductal carcinoma and ductal carcinoma in situ,respectively.For each subject,we generate second-derivative images that identify a network of highly absorbing structures in the breast that we assign to blood vessels.A previously developed paired-wavelength spectral method assigns oxygenation values to the absorbing structures displayed in the second-derivative images.The resulting oxygenation images feature average values over the whole breast that are significantly lower in cancerous breasts(69±14%,n=2)than in healthy breasts(85±7%,n=18)(p<0.01).Furthermore,in the two patients with breast cancer,the average oxygenation values in the cancerous regions are also significantly lower than in the remainder of the breast(invasive ductal carcinoma:49±11%vs 61±16%,p<0.01;ductal carcinoma in situ:58±8%vs 77±11%,p<0.001).
基金supported by the National Natural Science Foundation of China 62175153the Shanghai Science and Technology Commission 21S902700.
文摘Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.
基金funded by the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2018349)the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(No.2016r036)+2 种基金the Irmovation and Entrepreneurship Training Program Project for the Jiangsu College Students(No.2017103000165)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA05020200)the National Natural Science Foundation of China(No.91437220).
文摘Over recent decades, the global demand for food has continued to grow, owing to population growth and the loss of arable land. Rice ratooning offers new opportunities for increasing rice production and has received renewed interest because of the minimal additional labor input required for its adoption. Regular, regional-scale monitoring of the spatial patterns of both traditional and ratoon rice cropping systems provides essential information for agricultural resource management and food security studies. However, the similar phenological characteristics of traditional double rice and ratoon rice cropping systems make it challenging to accurately classify these cropping practices based on satellite observations alone. In this study, we first proposed an improved phenology-based rice cropping area detection algorithm using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) imagery. A new index, ratoon rice index, was then developed to automatically delineate ratoon rice cropping areas with the aid of a base map of rice in Hubei Province, China. The accuracy assessment using ground truth data showed that our approach could map both traditional and ratoon rice cropping areas with high user accuracy (91.25% and 91.43%, respectively). The MODIS-retrieved rice cropping areas were validated using annual agricultural census data, and coefficient of determination (R2) values of 0.60 and 0.41 were recorded for traditional and ratoon rice cropping systems, respectively. The total area of ratoon rice was estimated to be 1 283.6 km2, 5.0% of the total rice cropping area, in Hubei Province in 2016. These demonstrated the feasibility of extracting the spatial patterns of both traditional and ratoon rice cropping systems solely from time-series NDVI and field survey data and strides made in facilitating the timely and routine monitoring of traditional and ratoon rice distribution at subnational level. Given sufficient historical satellite and phenology records, the proposed algorithm had the potential to enhance rice cropping area mapping efforts across a broad temporal scale (e.g., from the 1980s to the present).
