The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications.As two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral ...The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications.As two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral images(HSIs)have complementary characteristics.The MSI has a large swath and short revisit period,but the number of bands is limited with low spectral resolution,leading to weak separability of between class spectra.Compared with MSI,HSI has hundreds of bands and each of them is narrow in bandwidth,which enable it to have the ability of fine classification,but too long in aspects of revisit period.To make efficient use of their combined advantages,multispectral-hyperspectral remote sensing image collaborative classification has become one of hot topics in remote sensing.To deal with the collaborative classification,most of current methods are unsupervised and only consider the HSI reconstruction as the objective.In this paper,a class-guided coupled dictionary learning method is proposed,which is obviously distinguished from the current methods.Specifically,the proposed method utilizes the labels of training samples to construct discriminative sparse representation coefficient error and classification error as regularization terms,so as to enforce the learned coupled dictionaries to be both representational and discriminative.The learned coupled dictionaries facilitate pixels from the same category have similar sparse represent coefficients,while pixels from different categories have different sparse represent coefficients.The experiments on three pairs of HSI and MSI have shown better classification performance.展开更多
Seismic hazards pose a major threat to life safety,social development,and the economy.Traditional seismic vulnerability and risk assessments,such as field survey methods,may not be suitable for densely built-up urban ...Seismic hazards pose a major threat to life safety,social development,and the economy.Traditional seismic vulnerability and risk assessments,such as field survey methods,may not be suitable for densely built-up urban areas due to the limited availability of comprehensive data and potential subjectivity in judgment.To overcome these limitations,an integrated method for seismic vulnerability and risk assessment based on multimodal remote sensing data,support vector machine(SVM)and GIScience methods was proposed and applied to the central urban area of Jinan City,Shandong Province,China.First,an area with representative buildings was selected for field survey research,and an attribute information base established.Then,the SVM method was used to establish the susceptibility proxies,which were applied to the whole study area after accuracy evaluation.Finally,the spatial distribution of seismic vulnerability and risk under different seismic intensity scenarios(from VI to X)was analyzed in GIScience.The results show that the average building vulnerability index in the central urban area of Jinan City is 0.53,indicating that the overall seismic performance of buildings is at a moderate level.Under the seismic intensity scenario of VIII,the buildings in the Starting area and New urban district of Jinan would mostly suffer‘Moderate’damage,while Old urban areas,with more seismic-resistant buildings,would experience only‘Slight’damage.This study aims to offer an efficient and accurate method for assessing seismic vulnerability in mid to large-sized cities characterized by concentrated population densities and rapid urbanization,as well as provide a valuable reference for efforts in urban renewal,seismic mitigation,and land planning,particularly in cities and regions of developing countries.Additionally,it contributes to the realization of Sustainable Development Goal 11,which seeks to make cities and human settlements inclusive,safe,resilient,and sustainable.展开更多
基金supported by the National Natural Youth Science Foundation Project (Grant No. 62001142)the Key International Cooperation Project (Grant No. 61720106002)+1 种基金the Distinguished Young Scholars of National Natural Science Foundation of China (Grant No. 62025107)Heilongjiang Postdoctoral Fund (Grant No. LBH-Z20068)
文摘The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications.As two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral images(HSIs)have complementary characteristics.The MSI has a large swath and short revisit period,but the number of bands is limited with low spectral resolution,leading to weak separability of between class spectra.Compared with MSI,HSI has hundreds of bands and each of them is narrow in bandwidth,which enable it to have the ability of fine classification,but too long in aspects of revisit period.To make efficient use of their combined advantages,multispectral-hyperspectral remote sensing image collaborative classification has become one of hot topics in remote sensing.To deal with the collaborative classification,most of current methods are unsupervised and only consider the HSI reconstruction as the objective.In this paper,a class-guided coupled dictionary learning method is proposed,which is obviously distinguished from the current methods.Specifically,the proposed method utilizes the labels of training samples to construct discriminative sparse representation coefficient error and classification error as regularization terms,so as to enforce the learned coupled dictionaries to be both representational and discriminative.The learned coupled dictionaries facilitate pixels from the same category have similar sparse represent coefficients,while pixels from different categories have different sparse represent coefficients.The experiments on three pairs of HSI and MSI have shown better classification performance.
基金supported in part by the National Natural Science Foundation of China(Grant No.42201077)the Natural Science Foundation of Shandong Province(No.ZR2021QD074)+2 种基金the China Postdoctoral Science Foundation(No.2023M732105)the Lhasa National Geophysical Observation and Research Station(No.NORSLS22-05)the Youth Innovation Team Project of Higher School in Shandong Province,China(No.2024KJH087).
文摘Seismic hazards pose a major threat to life safety,social development,and the economy.Traditional seismic vulnerability and risk assessments,such as field survey methods,may not be suitable for densely built-up urban areas due to the limited availability of comprehensive data and potential subjectivity in judgment.To overcome these limitations,an integrated method for seismic vulnerability and risk assessment based on multimodal remote sensing data,support vector machine(SVM)and GIScience methods was proposed and applied to the central urban area of Jinan City,Shandong Province,China.First,an area with representative buildings was selected for field survey research,and an attribute information base established.Then,the SVM method was used to establish the susceptibility proxies,which were applied to the whole study area after accuracy evaluation.Finally,the spatial distribution of seismic vulnerability and risk under different seismic intensity scenarios(from VI to X)was analyzed in GIScience.The results show that the average building vulnerability index in the central urban area of Jinan City is 0.53,indicating that the overall seismic performance of buildings is at a moderate level.Under the seismic intensity scenario of VIII,the buildings in the Starting area and New urban district of Jinan would mostly suffer‘Moderate’damage,while Old urban areas,with more seismic-resistant buildings,would experience only‘Slight’damage.This study aims to offer an efficient and accurate method for assessing seismic vulnerability in mid to large-sized cities characterized by concentrated population densities and rapid urbanization,as well as provide a valuable reference for efforts in urban renewal,seismic mitigation,and land planning,particularly in cities and regions of developing countries.Additionally,it contributes to the realization of Sustainable Development Goal 11,which seeks to make cities and human settlements inclusive,safe,resilient,and sustainable.
