Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still...Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software’s to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.展开更多
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial l...A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.展开更多
Detecting ink mismatch is a significant challenge in verifying the authenticity of documents,especially when dealing with uneven ink distribution.Conventional imaging methods frequently fail to distinguish visually si...Detecting ink mismatch is a significant challenge in verifying the authenticity of documents,especially when dealing with uneven ink distribution.Conventional imaging methods frequently fail to distinguish visually similar inks.Our study presents a novel hyperspectral unmixing approach to detect ink mismatches in unbalanced clusters.The proposed method identifies unique spectral characteristics of different inks employing k-means clustering and Gaussian mixture models(GMMs)to perform color segmentation on different ink types and utilizes elbow estimation and silhouette coefficient to evaluate the number of inks estimation precisely.For a more accurate estimation of quantity,which is generally not an attribute of clustering methods,we employed entropy calculations in the red,green,and blue depth channels for precise abundance estimation of ink.This unique combination of basic techniques in conjunction exhibits better efficacy in performing ink unmixing and provides a real-world document forensic solution compared to current methods that rely on assumptions like prior knowledge of the inks used in a document and deep learning-based methods that rely heavily on abundant training datasets.We evaluate our approach on the ivision handwritten hyperspectral images dataset(iVision HHID),which is a comprehensive and rich dataset that surpasses the commonly-used UWA writing inks hyperspectral images(WIHSI)database in size and diversity.This study has accomplished the unmixing task with three main challenges:unmixing of diverse ink spectral signatures(149 spectral bands instead of 33 bands in the previous dataset),without using prior knowledge and assumptions about the number of inks used in the questioned document,and not requiring large training data for performing unmixing.Furthermore,the security of the proposed document authentication methodology to address the likelihood of forgeries or manipulations in questioned documents is enhanced as compared to previous works relying on known inks and known spectrum.Randomization techniques and anomaly detection mechanisms are used in our methodology which increases the difficulty for adversaries to predict and manipulate specific aspects of the input data in questioned documents,thereby enhancing the robustness of our method.The code for conducting this research can be accessed at GitHub repository.展开更多
对所获取的2008年冬季的辽东湾西岸海域含有海冰的Hyperion高光谱图像进行了大气校正,得到了反射率图像。用ISODATA(Iterative Self-Organizing Data Analysis Technique)聚类分析方法对反射率图像进行计算机自动分类,并结合实测的同时...对所获取的2008年冬季的辽东湾西岸海域含有海冰的Hyperion高光谱图像进行了大气校正,得到了反射率图像。用ISODATA(Iterative Self-Organizing Data Analysis Technique)聚类分析方法对反射率图像进行计算机自动分类,并结合实测的同时期的海冰反射率光谱确定了不同海冰类型的分布范围。根据不同类型海冰的厚度特征,得到了海冰厚度分级分布图和海冰厚度图。结果表明,Hyperion图像可以区分光谱有区别的冰型,无法区分浮冰和固定冰,可以更清晰地显示出海冰的光谱反射率,与实测光谱曲线更加相似,优于MODIS多光谱图像。同时,用主成分分析方法对海冰Hyperion图像进行了分析。海冰Hyperion图像中,各个波段之间的相关系数都较大,光谱维信息冗余度较大,其中30波段贡献率最高。展开更多
森林生态系统碳循环是目前全球变化研究中的一个热点问题,叶面积指数(leaf area index,LAI)是森林生态系统碳循环模型中的一个重要的输入参数。准确地获取LAI的空间分布对提高碳循环模型的模拟精度具有重要意义。高光谱影像反演LAI比多...森林生态系统碳循环是目前全球变化研究中的一个热点问题,叶面积指数(leaf area index,LAI)是森林生态系统碳循环模型中的一个重要的输入参数。准确地获取LAI的空间分布对提高碳循环模型的模拟精度具有重要意义。高光谱影像反演LAI比多光谱影像具有明显的优势。以福建永安重点林区为研究区,以EO—1 Hyperion高光谱影像为数据源开展森林LAI反演模型研究,在对不同类型植被指数以及不同近红外/红波段组合构建的植被指数与实测LAI相关性做综合分析比较的基础上,最终建立研究区高精度LAI反演模型。该研究对于提高福建乃至全国森林LAI反演精度和碳循环的模拟能力、增强国际竞争力具有重要的意义。展开更多
由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH(Fast Lin...由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH(Fast Line of Sight Atmospheric Analysis of Spectral Hyper-cubes)模块对新疆地区Hyperion高光谱遥感影像进行大气校正,并对处理结果进行评价,结果表明FLAASH模块大气纠正效果良好。展开更多
文摘Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software’s to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.
基金National Key Research and Development Program of China(No.2016YFF0103604)National Natural Science Foundations of China(Nos.61171165,11431015,61571230)+1 种基金National Scientific Equipment Developing Project of China(No.2012YQ050250)Natural Science Foundation of Jiangsu Province,China(No.BK20161500)
文摘A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.
文摘Detecting ink mismatch is a significant challenge in verifying the authenticity of documents,especially when dealing with uneven ink distribution.Conventional imaging methods frequently fail to distinguish visually similar inks.Our study presents a novel hyperspectral unmixing approach to detect ink mismatches in unbalanced clusters.The proposed method identifies unique spectral characteristics of different inks employing k-means clustering and Gaussian mixture models(GMMs)to perform color segmentation on different ink types and utilizes elbow estimation and silhouette coefficient to evaluate the number of inks estimation precisely.For a more accurate estimation of quantity,which is generally not an attribute of clustering methods,we employed entropy calculations in the red,green,and blue depth channels for precise abundance estimation of ink.This unique combination of basic techniques in conjunction exhibits better efficacy in performing ink unmixing and provides a real-world document forensic solution compared to current methods that rely on assumptions like prior knowledge of the inks used in a document and deep learning-based methods that rely heavily on abundant training datasets.We evaluate our approach on the ivision handwritten hyperspectral images dataset(iVision HHID),which is a comprehensive and rich dataset that surpasses the commonly-used UWA writing inks hyperspectral images(WIHSI)database in size and diversity.This study has accomplished the unmixing task with three main challenges:unmixing of diverse ink spectral signatures(149 spectral bands instead of 33 bands in the previous dataset),without using prior knowledge and assumptions about the number of inks used in the questioned document,and not requiring large training data for performing unmixing.Furthermore,the security of the proposed document authentication methodology to address the likelihood of forgeries or manipulations in questioned documents is enhanced as compared to previous works relying on known inks and known spectrum.Randomization techniques and anomaly detection mechanisms are used in our methodology which increases the difficulty for adversaries to predict and manipulate specific aspects of the input data in questioned documents,thereby enhancing the robustness of our method.The code for conducting this research can be accessed at GitHub repository.
文摘对所获取的2008年冬季的辽东湾西岸海域含有海冰的Hyperion高光谱图像进行了大气校正,得到了反射率图像。用ISODATA(Iterative Self-Organizing Data Analysis Technique)聚类分析方法对反射率图像进行计算机自动分类,并结合实测的同时期的海冰反射率光谱确定了不同海冰类型的分布范围。根据不同类型海冰的厚度特征,得到了海冰厚度分级分布图和海冰厚度图。结果表明,Hyperion图像可以区分光谱有区别的冰型,无法区分浮冰和固定冰,可以更清晰地显示出海冰的光谱反射率,与实测光谱曲线更加相似,优于MODIS多光谱图像。同时,用主成分分析方法对海冰Hyperion图像进行了分析。海冰Hyperion图像中,各个波段之间的相关系数都较大,光谱维信息冗余度较大,其中30波段贡献率最高。
文摘森林生态系统碳循环是目前全球变化研究中的一个热点问题,叶面积指数(leaf area index,LAI)是森林生态系统碳循环模型中的一个重要的输入参数。准确地获取LAI的空间分布对提高碳循环模型的模拟精度具有重要意义。高光谱影像反演LAI比多光谱影像具有明显的优势。以福建永安重点林区为研究区,以EO—1 Hyperion高光谱影像为数据源开展森林LAI反演模型研究,在对不同类型植被指数以及不同近红外/红波段组合构建的植被指数与实测LAI相关性做综合分析比较的基础上,最终建立研究区高精度LAI反演模型。该研究对于提高福建乃至全国森林LAI反演精度和碳循环的模拟能力、增强国际竞争力具有重要的意义。
文摘由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH(Fast Line of Sight Atmospheric Analysis of Spectral Hyper-cubes)模块对新疆地区Hyperion高光谱遥感影像进行大气校正,并对处理结果进行评价,结果表明FLAASH模块大气纠正效果良好。
