In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring meth...In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.展开更多
Remote sensing is an effective way in monitoring desertification dynamics in arid and semi-arid regions.In this study,we used a decision tree method based on NDVI(normalized difference vegetation index),SAVI(soil adju...Remote sensing is an effective way in monitoring desertification dynamics in arid and semi-arid regions.In this study,we used a decision tree method based on NDVI(normalized difference vegetation index),SAVI(soil adjusted vegetation index),and vegetation cover proportion to quantify and analyze the desertification in Eritrea using Landsat data of the 1970 s,1980 s and 2014.The results demonstrate that the NDVI value and the annual mean precipitation declined while the temperature increased over the past 40 a.Strongly desertified land increased from 4.82×10^4 km^2(38.5%)in the 1970 s to 8.38×10^4 km^2(66.9%)in 2014:approximately 85%of the land of the country was under serious desertification,which significantly occurred in arid and semi-arid lowlands of the country(eastern,northern,and western lowlands)with relatively scarce precipitation and high temperature.The non-desertified area,mostly located in the sub-humid eastern escarpment,also declined from approximately 2.1%to 0.5%.The study concludes that the desertification is a cause of serious land degradation in Eritrea and may link to climate changes,such as low and unpredictable precipitation,and prolonged drought.展开更多
Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal...Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.展开更多
In this paper, the theory of plausible and paradoxical reasoning of Dezert- Smarandache (DSmT) is used to take into account the paradoxical charac-ter through the intersections of vegetation, aquatic and mineral surfa...In this paper, the theory of plausible and paradoxical reasoning of Dezert- Smarandache (DSmT) is used to take into account the paradoxical charac-ter through the intersections of vegetation, aquatic and mineral surfaces. In order to do this, we developed a classification model of pixels by aggregating information using the DSmT theory based on the PCR5 rule using the ∩NDVI, ∩MNDWI and ∩NDBaI spectral indices obtained from the ASTER satellite images. On the qualitative level, the model produced three simple classes for certain knowledge (E, V, M) and eight composite classes including two union classes characterizing partial ignorance ({E,V}, {M,V}) and six classes of intersection of which three classes of simple intersection (E∩V, M∩V, E∩M) and three classes of composite intersection (E∩{M,V}, M∩{E,V}, V∩{E,M}), which represent paradoxes. This model was validated with an average rate of 93.34% for the well-classified pixels and a compliance rate of the entities in the field of 96.37%. Thus, the model 1 retained provides 84.98% for the simple classes against 15.02% for the composite classes.展开更多
Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are s...Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.展开更多
研究波段参数对NDVI估算植被生物物理参数的影响,对于提高NDVI在植被覆盖变化监测中的应用精度具有重要意义。采用无人机载Resonon Pika XC2高光谱仪获取的人工草地高光谱影像,分析红光和近红外波段位置移动与宽度变化对NDVI的影响,评估...研究波段参数对NDVI估算植被生物物理参数的影响,对于提高NDVI在植被覆盖变化监测中的应用精度具有重要意义。采用无人机载Resonon Pika XC2高光谱仪获取的人工草地高光谱影像,分析红光和近红外波段位置移动与宽度变化对NDVI的影响,评估NDVI对植被盖度的敏感性和植被盖度估算精度。结果表明:波段位置固定时红光和近红外波段宽度扩展对NDVI及其敏感性影响不大,窄波段NDVI估算植被盖度的精度优于宽波段。红光和近红外波段位置向长波方向移动时对NDVI及其敏感性有不同程度的影响,随着敏感性增强NDVI抗扰动性降低,估算植被盖度的精度有所下降。窄波段NDVI的灵敏度系数及其与植被盖度线性拟合的R^(2)波动剧烈,植被盖度估算的位置稳定性较差。10 nm NDVI在不同位置处取得了较高的盖度估算精度,R^(2)最大值为0.83。4种主流卫星影像计算的宽波段NDVI对于高植被覆盖区盖度反演具有良好的适用性,但与窄波段10 nm NDVI相比其盖度反演精度仍然有一定程度的衰减。研究结果可为NDVI精确反演植被参数提供科学参考和依据。展开更多
基金provided by the Program for New Century Excellent Talents in University (No. NCET-06-0477)the Independent Research Project of the State Key Laboratory of Coal Resources and Mine Safety of China University of Mining and Technology (No. SKLCRSM09X01)the Fundamental Research Funds for the Central Universities
文摘In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.
基金supported by the National Natural Science Foundation of China (41271024)
文摘Remote sensing is an effective way in monitoring desertification dynamics in arid and semi-arid regions.In this study,we used a decision tree method based on NDVI(normalized difference vegetation index),SAVI(soil adjusted vegetation index),and vegetation cover proportion to quantify and analyze the desertification in Eritrea using Landsat data of the 1970 s,1980 s and 2014.The results demonstrate that the NDVI value and the annual mean precipitation declined while the temperature increased over the past 40 a.Strongly desertified land increased from 4.82×10^4 km^2(38.5%)in the 1970 s to 8.38×10^4 km^2(66.9%)in 2014:approximately 85%of the land of the country was under serious desertification,which significantly occurred in arid and semi-arid lowlands of the country(eastern,northern,and western lowlands)with relatively scarce precipitation and high temperature.The non-desertified area,mostly located in the sub-humid eastern escarpment,also declined from approximately 2.1%to 0.5%.The study concludes that the desertification is a cause of serious land degradation in Eritrea and may link to climate changes,such as low and unpredictable precipitation,and prolonged drought.
基金the Frontier Program of the Knowledge Innovation Program of Chinese Academy of Sciences
文摘Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.
文摘In this paper, the theory of plausible and paradoxical reasoning of Dezert- Smarandache (DSmT) is used to take into account the paradoxical charac-ter through the intersections of vegetation, aquatic and mineral surfaces. In order to do this, we developed a classification model of pixels by aggregating information using the DSmT theory based on the PCR5 rule using the ∩NDVI, ∩MNDWI and ∩NDBaI spectral indices obtained from the ASTER satellite images. On the qualitative level, the model produced three simple classes for certain knowledge (E, V, M) and eight composite classes including two union classes characterizing partial ignorance ({E,V}, {M,V}) and six classes of intersection of which three classes of simple intersection (E∩V, M∩V, E∩M) and three classes of composite intersection (E∩{M,V}, M∩{E,V}, V∩{E,M}), which represent paradoxes. This model was validated with an average rate of 93.34% for the well-classified pixels and a compliance rate of the entities in the field of 96.37%. Thus, the model 1 retained provides 84.98% for the simple classes against 15.02% for the composite classes.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61772295 and 61572270)the PHD foundation of Chongqing Normal University (Grant No.19XLB003)Chongqing Technology Foresight and Institutional Innovation Project (Grant No.cstc2021jsyjyzysbAX0011)。
文摘Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.
