The thermal-environment effect exists in the field of rapid urbanization. It has adverse effects on the urban atmosphere, re- gional climate, energy consumption, and public health. Shenzhen, a representative of rapidl...The thermal-environment effect exists in the field of rapid urbanization. It has adverse effects on the urban atmosphere, re- gional climate, energy consumption, and public health. Shenzhen, a representative of rapidly urbanizing cities in China, was selected as a case for pattern dynamics analysis of the thermal environment. The surface temperature was acquired from the thermal infrared data of Landsat TM and ETM＋ images in 1986, 1995, and 2005 by Jim6nez-Mufioz and Sobrino＇s generalized single-channel method, which was used in assessing the distribution and spatial patterns of the thermal environment. The relative thermal environment curve （RTC） was combined with Moran＇s I analysis to assess the pattern dynamics of the thermal environment in different urbanization periods. Moran＇s I index and the RTC represent a process of aggregation-fragmentation-aggregation, which shows the aggregation pattern of a decrease during the rapid urbanization period and then an increase during the steady urbanization period. High-temperature areas gradually ex- panded to a uniform and scattered distribution in the rapid urbanization period; while the high thermal-environment effect was gradually transformed into a steady spatial pattern in the stable urbanization period. To characterize the increasing development in this multiple- center city, we chose profiles along an urban-development axis. The results suggest that heat islands have expanded from internal urban to external urban areas. Four profiles were obtained showing differences in shape due to spatial differences in the process of development.展开更多
The use of a general EM(expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems:singularity of the variance-covariance matrix and sensitivity of randomly selected init...The use of a general EM(expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems:singularity of the variance-covariance matrix and sensitivity of randomly selected initial values.The former causes computation failure;the latter produces unstable classification results.This paper proposes a modified approach to resolve these defects.First,a modification is proposed to determine reliable parameters for the EM algorithm based on a k-means algorithm with initial centers obtained from the density function of the first principal component,which avoids the selection of initial centers at random.A second modification uses the principal component transformation of the image to obtain a set of uncorrelated data.The number of principal components as the input of the EM algorithm is determined by the principal contribution rate.In this way,the modification can not only remove singularity but also weaken noise.Experimental results obtained from two sets of remote sensing images acquired by two different sensors confirm the validity of the proposed approach.展开更多
基金Under the auspices of National Natural Science Foundation of China (No. 41101175,40635028)
文摘The thermal-environment effect exists in the field of rapid urbanization. It has adverse effects on the urban atmosphere, re- gional climate, energy consumption, and public health. Shenzhen, a representative of rapidly urbanizing cities in China, was selected as a case for pattern dynamics analysis of the thermal environment. The surface temperature was acquired from the thermal infrared data of Landsat TM and ETM＋ images in 1986, 1995, and 2005 by Jim6nez-Mufioz and Sobrino＇s generalized single-channel method, which was used in assessing the distribution and spatial patterns of the thermal environment. The relative thermal environment curve （RTC） was combined with Moran＇s I analysis to assess the pattern dynamics of the thermal environment in different urbanization periods. Moran＇s I index and the RTC represent a process of aggregation-fragmentation-aggregation, which shows the aggregation pattern of a decrease during the rapid urbanization period and then an increase during the steady urbanization period. High-temperature areas gradually ex- panded to a uniform and scattered distribution in the rapid urbanization period; while the high thermal-environment effect was gradually transformed into a steady spatial pattern in the stable urbanization period. To characterize the increasing development in this multiple- center city, we chose profiles along an urban-development axis. The results suggest that heat islands have expanded from internal urban to external urban areas. Four profiles were obtained showing differences in shape due to spatial differences in the process of development.
基金supported by the National High-tech R&D Program of China(2007AA12Z226 and SS2012AA120804)the National Natural Science Foundation of China(40674015 and 41074009)+2 种基金the Doctoral Fund of Ministry of Education of China(20100022110008)the Fundamental Research Funds for the Central Universities(2-9-2011-227)the Open Research Fund of Key Laboratory of Digital Earth Science,Center for Earth Observation and Digital Earth,Chinese Academy of Sciences (2010LDE002)
文摘The use of a general EM(expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems:singularity of the variance-covariance matrix and sensitivity of randomly selected initial values.The former causes computation failure;the latter produces unstable classification results.This paper proposes a modified approach to resolve these defects.First,a modification is proposed to determine reliable parameters for the EM algorithm based on a k-means algorithm with initial centers obtained from the density function of the first principal component,which avoids the selection of initial centers at random.A second modification uses the principal component transformation of the image to obtain a set of uncorrelated data.The number of principal components as the input of the EM algorithm is determined by the principal contribution rate.In this way,the modification can not only remove singularity but also weaken noise.Experimental results obtained from two sets of remote sensing images acquired by two different sensors confirm the validity of the proposed approach.
