This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformatio...This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.展开更多
Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initial...Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.展开更多
Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics simila...Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.展开更多
针对目前草原植被盖度和物候期监测中存在的连续工作能力差、自动监测能力弱和精确度较低问题,将固定监测、移动监测和云平台结合,研制了一种草原植被盖度与物候智能监测系统。该系统主要由固定监测子系统、移动监测子系统以及草原物候...针对目前草原植被盖度和物候期监测中存在的连续工作能力差、自动监测能力弱和精确度较低问题,将固定监测、移动监测和云平台结合,研制了一种草原植被盖度与物候智能监测系统。该系统主要由固定监测子系统、移动监测子系统以及草原物候智能监测云平台组成。固定监测子系统主要由物候相机、供电模块、通信模块、边缘计算控制器和支撑立杆等组成,移动监测子系统主要包括手持机和应用程序。草原物候智能监测云平台基于浏览器/服务器模式架构设计,具有信息查询、数据分析、数据显示和数据共享等功能。固定监测子系统和移动监测子系统可实现草原植被图像数据的采集和上传,然后通过云服务器部署的图像处理程序自动提取草原植被指数和植被盖度并存入数据库。在此基础上,通过拟合植被指数的时间序列获得植被生长曲线,并利用TIMESAT软件提取物候参数。经测试,提出的利用过绿指数(excess green index,EXG)结合最大类间方差法分割草原植被图像进而实现草原植被盖度识别的方法获得了90%的精确度,满足草原植被盖度自动化和批量化提取需求。并且,该研究在提取相对绿度指数(green chromatic coordinate,GCC)、EXG与归一化红绿差分指数(normalized green red difference index,NGRDI)植被指数的基础上,采用Double Logistic函数拟合的植被生长曲线可以准确反映植被生长周期。该系统为草原植被数智化监测和管理提供了可靠的技术和数据支撑。展开更多
Image is an important resource on WWW.Many software tools index the text on WWW,but little attention has been paid to the image.The article introduces a method of indexing the captions of images on WWW by semantic ana...Image is an important resource on WWW.Many software tools index the text on WWW,but little attention has been paid to the image.The article introduces a method of indexing the captions of images on WWW by semantic analysis and image content analysis.展开更多
基金Project supported by the National Oommission of Defense Science and Technotocjy(No.Y96-10)
文摘This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.
基金supported by the Research Management Center,Xiamen University Malaysia under XMUM Research Program Cycle 4(Grant No:XMUMRF/2019-C4/IECE/0012).
文摘Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.
基金supported by the National Natural Science Foundation of China(Grant No.42271078)the Key Research and Development Program of Shaanxi(Grant No.2024SF-YBXM669)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0902)。
文摘Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.
文摘针对目前草原植被盖度和物候期监测中存在的连续工作能力差、自动监测能力弱和精确度较低问题,将固定监测、移动监测和云平台结合,研制了一种草原植被盖度与物候智能监测系统。该系统主要由固定监测子系统、移动监测子系统以及草原物候智能监测云平台组成。固定监测子系统主要由物候相机、供电模块、通信模块、边缘计算控制器和支撑立杆等组成,移动监测子系统主要包括手持机和应用程序。草原物候智能监测云平台基于浏览器/服务器模式架构设计,具有信息查询、数据分析、数据显示和数据共享等功能。固定监测子系统和移动监测子系统可实现草原植被图像数据的采集和上传,然后通过云服务器部署的图像处理程序自动提取草原植被指数和植被盖度并存入数据库。在此基础上,通过拟合植被指数的时间序列获得植被生长曲线,并利用TIMESAT软件提取物候参数。经测试,提出的利用过绿指数(excess green index,EXG)结合最大类间方差法分割草原植被图像进而实现草原植被盖度识别的方法获得了90%的精确度,满足草原植被盖度自动化和批量化提取需求。并且,该研究在提取相对绿度指数(green chromatic coordinate,GCC)、EXG与归一化红绿差分指数(normalized green red difference index,NGRDI)植被指数的基础上,采用Double Logistic函数拟合的植被生长曲线可以准确反映植被生长周期。该系统为草原植被数智化监测和管理提供了可靠的技术和数据支撑。
文摘Image is an important resource on WWW.Many software tools index the text on WWW,but little attention has been paid to the image.The article introduces a method of indexing the captions of images on WWW by semantic analysis and image content analysis.
