The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problem...The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.展开更多
Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free a...Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.展开更多
Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and diffe...Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.展开更多
In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided. Meanwhile, wa...In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided. Meanwhile, wavelet decomposition has a property of frequency division in transform domain. And the statistical property of wavelet coefficient reflects those significant features. So, a united optimal fusion method, which using the statistical property of wavelet decomposition and IHS transform on pixel and展开更多
We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral grap...We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral graph. The contribution may be divided into three parts. Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model. Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features. Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.展开更多
This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms wer...This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms were used to extract texture feature of the image and the average color was used to extract the color features. The principle component of the feature vector of image can be constructed. Content based image retrieval was performed by comparing the feature vector of the query image with the projection feature vector of the image database on the principle component space of the query image. By this technique, it can reduce the dimensionality of feature vector, which in turn reduce the searching time.展开更多
Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor dif...Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.展开更多
Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25...Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25% of the total slums of Morocco [1]. These are the habitats of all deprived of healthy sanitary conditions and judged precarious from the perspective humanitarian and below the acceptable. The majority of the inhabi- tants of these slums are from the rural exodus with insufficient income to meet the basic needs of daily life. Faced with this situation and to eradicate these habitats, the Moroccan government has launched since 2004 an entire program to create cities without slums (C.W.S.) to resettle or relocate families. Indeed the process control and monitoring of this program requires first identifying and detecting spatial habitats. To achieve these tasks, conventional methods such as information gathering, mapping, use of databases and statistics often have shown their limits and are sometimes outdated. It is within this framework and that of the great German Morocco project “Urban agriculture as an integrative factor of development that fits our project de- tection of slums in Casablanca. The use of satellite imagery, particulary the HSR, has the advantage of providing the physical coverage of urban land but it raises the difficulty of choosing the appropriate method to apply.This paper is actually to develop new approaches based mainly on object-oriented classification of high spatial resolution satellite images for the detection of slums.This approach has been developed for mapping the urban land through by integration of several types of information (spectral, spatial, contextual ...) (Hofmann, P ., 2001, Herold et al. 2002b;Van Der Sande et al., 2003, Benz et al., 2004, Nobrega et al., 2006). In order to refine the result of classification, we applied mathematical morphology and in particular the closing filter. The data from this classification (binary image), which then will be used in a spatial data- base (ArcGIS).展开更多
Solid organic matter is an important constituent not only in coal, but also in black shale-hosted ore deposits. The reliable recognition and quantification of organic carbon—as well as its microfabric relation to ass...Solid organic matter is an important constituent not only in coal, but also in black shale-hosted ore deposits. The reliable recognition and quantification of organic carbon—as well as its microfabric relation to associated inorganic minerals—plays a crucial role in characterization by scanning electron microscopy-based image analysis. However, the use of conventional epoxy resin in the preparation of grain mounts does not allow for recognition of solid organic carbon compounds. In this study we illustrate that the use of iodized epoxy resin readily overcomes this bottleneck. Best results are obtained with an addition of 15 wt% iodoform to the epoxy resin. With process samples of black shale-hosted polymetallic Kupferschiefer-type ore as a case study, it is shown that recognition and quantification of solid organic carbon are easily achieved and that tangible parameters such as particle and grain sizes, association and liberation for ore and gangue minerals can be determined in the presence of solid organic matter. Due to the inherent uncertainty of the exact chemical composition of the kerogen contained in Kupferschiefer, it was not possible to attain exact comparability between chemical Corg assays and assays calculated from MLA data. However, the results are still found to closely agree with one another. The strength of iodized resin lies in its ability to distinguish organic matter with high hydration ratios in addition to the easy integration in sample preparation. It could therefore be an attractive supplement in the analyses of other raw materials containing complex organic-matter.展开更多
Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to ...Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.展开更多
随着特高压直流输电技术飞速发展,换流阀阀基电子VBE(valve base electronics)设备的稳定性对于保障直流输电的可靠性和效率至关重要。VBE设备电路板缺陷,如短路和失效元件,直接影响直流系统稳定性,而现有的检测方法,包括人工显微镜检...随着特高压直流输电技术飞速发展,换流阀阀基电子VBE(valve base electronics)设备的稳定性对于保障直流输电的可靠性和效率至关重要。VBE设备电路板缺陷,如短路和失效元件,直接影响直流系统稳定性,而现有的检测方法,包括人工显微镜检查和自动检测算法,常受限于效率低和准确性不足。针对该问题,提出一种基于点模式匹配的自动视觉检测方法,通过生成代表关键区域的点模式并进行匹配来提高检测的效率和准确率。通过实验验证,所提方法在检测速度和准确性方面相较于传统方法有显著提升,适合于生产线上的快速质量控制,为提高直流输电设备的质量提供了有效的技术方案。展开更多
Diffusion tensor imaging(DTI)provides a unique method to reveal the integrity of white matter microstructure noninvasively.Voxel-based analysis(VBA),which is a highly reproducible and user-independent technique,has be...Diffusion tensor imaging(DTI)provides a unique method to reveal the integrity of white matter microstructure noninvasively.Voxel-based analysis(VBA),which is a highly reproducible and user-independent technique,has been used to analyze DTI data in a number of studies.Fractional anisotropy(FA),which is derived from DTI,is the most frequently used parameter.The parameter setting during the DTI data preprocessing might affect the FA analysis results.However,there is no reliable evidence on how the parameters affect the results of FA analysis.This study sought to quantitatively investigate the factors that might affect the voxel-based analysis of FA;these include the interpolation during spatial normalization,smoothing kernel and statistical threshold.Because it is difficult to obtain the true information of the lesion in the patients,we simulated lesions on the healthy FA maps.The DTI data were obtained from 20 healthy subjects.The FA maps were calculated using DTIStudio.We randomly divided these FA maps into two groups.One was used as a model patient group,and the other was used as a normal control group.Simulated lesions were added to the model patient group by decreasing the FA intensities in a specified region by 5%–50%.The model patient group and the normal control group were compared by two-sample t test statistic analysis voxelby-voxel to detect the simulated lesions.We evaluated these factors by comparing the difference between the detected lesion through VBA and the simulated lesion.The result showed that the space normalization of FA image should use the trilinear interpolation,and the smoothing kernel should be 2–3 times the voxel size of spatially normalized FA image.For lesions with small intensity change,FWE correction must be cautiously used.This study provided an important reference to the analysis of FA with VBA method.展开更多
文摘The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402003)the CAS Earth Big Data Science Project(No.XDA19060303)the Innovation Project of the State Key Laboratory of Resources and Environmental Information System(No.O88RAA01YA)
文摘Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.
文摘Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.
基金This work was jointly supported by the National Natural Science Foundation of China (No. 60375008), China National '863' Project (No. 2001AA135091), Shanghai Key Scientific Project (No. 02DZ15001), Aviation Science Foundation (No. 02D57003), and China Ph
文摘In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided. Meanwhile, wavelet decomposition has a property of frequency division in transform domain. And the statistical property of wavelet coefficient reflects those significant features. So, a united optimal fusion method, which using the statistical property of wavelet decomposition and IHS transform on pixel and
基金supported by the National Natural Science Foundation of China (No.60375003)the Aeronautics and Astronautics Basal Science Foundation of China (No.03I53059)the Science and Technology Innovation Foundation of Northwestern Polytechnical University (No.2007KJ01033)
文摘We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral graph. The contribution may be divided into three parts. Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model. Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features. Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.
文摘This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms were used to extract texture feature of the image and the average color was used to extract the color features. The principle component of the feature vector of image can be constructed. Content based image retrieval was performed by comparing the feature vector of the query image with the projection feature vector of the image database on the principle component space of the query image. By this technique, it can reduce the dimensionality of feature vector, which in turn reduce the searching time.
文摘Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.
文摘Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25% of the total slums of Morocco [1]. These are the habitats of all deprived of healthy sanitary conditions and judged precarious from the perspective humanitarian and below the acceptable. The majority of the inhabi- tants of these slums are from the rural exodus with insufficient income to meet the basic needs of daily life. Faced with this situation and to eradicate these habitats, the Moroccan government has launched since 2004 an entire program to create cities without slums (C.W.S.) to resettle or relocate families. Indeed the process control and monitoring of this program requires first identifying and detecting spatial habitats. To achieve these tasks, conventional methods such as information gathering, mapping, use of databases and statistics often have shown their limits and are sometimes outdated. It is within this framework and that of the great German Morocco project “Urban agriculture as an integrative factor of development that fits our project de- tection of slums in Casablanca. The use of satellite imagery, particulary the HSR, has the advantage of providing the physical coverage of urban land but it raises the difficulty of choosing the appropriate method to apply.This paper is actually to develop new approaches based mainly on object-oriented classification of high spatial resolution satellite images for the detection of slums.This approach has been developed for mapping the urban land through by integration of several types of information (spectral, spatial, contextual ...) (Hofmann, P ., 2001, Herold et al. 2002b;Van Der Sande et al., 2003, Benz et al., 2004, Nobrega et al., 2006). In order to refine the result of classification, we applied mathematical morphology and in particular the closing filter. The data from this classification (binary image), which then will be used in a spatial data- base (ArcGIS).
文摘Solid organic matter is an important constituent not only in coal, but also in black shale-hosted ore deposits. The reliable recognition and quantification of organic carbon—as well as its microfabric relation to associated inorganic minerals—plays a crucial role in characterization by scanning electron microscopy-based image analysis. However, the use of conventional epoxy resin in the preparation of grain mounts does not allow for recognition of solid organic carbon compounds. In this study we illustrate that the use of iodized epoxy resin readily overcomes this bottleneck. Best results are obtained with an addition of 15 wt% iodoform to the epoxy resin. With process samples of black shale-hosted polymetallic Kupferschiefer-type ore as a case study, it is shown that recognition and quantification of solid organic carbon are easily achieved and that tangible parameters such as particle and grain sizes, association and liberation for ore and gangue minerals can be determined in the presence of solid organic matter. Due to the inherent uncertainty of the exact chemical composition of the kerogen contained in Kupferschiefer, it was not possible to attain exact comparability between chemical Corg assays and assays calculated from MLA data. However, the results are still found to closely agree with one another. The strength of iodized resin lies in its ability to distinguish organic matter with high hydration ratios in addition to the easy integration in sample preparation. It could therefore be an attractive supplement in the analyses of other raw materials containing complex organic-matter.
文摘Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.
文摘随着特高压直流输电技术飞速发展,换流阀阀基电子VBE(valve base electronics)设备的稳定性对于保障直流输电的可靠性和效率至关重要。VBE设备电路板缺陷,如短路和失效元件,直接影响直流系统稳定性,而现有的检测方法,包括人工显微镜检查和自动检测算法,常受限于效率低和准确性不足。针对该问题,提出一种基于点模式匹配的自动视觉检测方法,通过生成代表关键区域的点模式并进行匹配来提高检测的效率和准确率。通过实验验证,所提方法在检测速度和准确性方面相较于传统方法有显著提升,适合于生产线上的快速质量控制,为提高直流输电设备的质量提供了有效的技术方案。
基金supported by the National Natural Science Foundation of China(81201147,91232713)the XieJialin Foundation of IHEP(3546370U2)foundation of IHEP(Y2515580U1)
文摘Diffusion tensor imaging(DTI)provides a unique method to reveal the integrity of white matter microstructure noninvasively.Voxel-based analysis(VBA),which is a highly reproducible and user-independent technique,has been used to analyze DTI data in a number of studies.Fractional anisotropy(FA),which is derived from DTI,is the most frequently used parameter.The parameter setting during the DTI data preprocessing might affect the FA analysis results.However,there is no reliable evidence on how the parameters affect the results of FA analysis.This study sought to quantitatively investigate the factors that might affect the voxel-based analysis of FA;these include the interpolation during spatial normalization,smoothing kernel and statistical threshold.Because it is difficult to obtain the true information of the lesion in the patients,we simulated lesions on the healthy FA maps.The DTI data were obtained from 20 healthy subjects.The FA maps were calculated using DTIStudio.We randomly divided these FA maps into two groups.One was used as a model patient group,and the other was used as a normal control group.Simulated lesions were added to the model patient group by decreasing the FA intensities in a specified region by 5%–50%.The model patient group and the normal control group were compared by two-sample t test statistic analysis voxelby-voxel to detect the simulated lesions.We evaluated these factors by comparing the difference between the detected lesion through VBA and the simulated lesion.The result showed that the space normalization of FA image should use the trilinear interpolation,and the smoothing kernel should be 2–3 times the voxel size of spatially normalized FA image.For lesions with small intensity change,FWE correction must be cautiously used.This study provided an important reference to the analysis of FA with VBA method.
