The noises of remote sensing images, caused by imaging system and ground environment, negatively affect the accuracy and efficiency in extracting forest information from remote sensing images. The denoising is critica...The noises of remote sensing images, caused by imaging system and ground environment, negatively affect the accuracy and efficiency in extracting forest information from remote sensing images. The denoising is critical for image classifications for forest areas. The objective of this research is to assess the effectiveness of currently used spatial filtering methods for extracting with forest information related from Landsat 5 TM images. Five spatial filtering methods including low-pass filter, median filter, mean filter, sigma filter and enhanced self-adaptive filter were examined. A set of evaluation indices was designed to assess the ability of each denoising method for flatness, edge/boundary retention and enhancement. Based on the designed evaluation indices and visual assessment, it was found that sigma filter （D=1） and enhanced self-adaptive filter were the most effective denoising methods in classifying TM images for forest areas.展开更多
中间表示对编译器的性能、效率和可扩展性都起着决定性作用.Open64编译器采用一种树型结构的中间表示WHIRL,能表示各种高级控制流结构,但不能显式的提供数据流信息.本文基于WHIRL对SSA(Static Single Assignment)进行扩展,提出了一个新...中间表示对编译器的性能、效率和可扩展性都起着决定性作用.Open64编译器采用一种树型结构的中间表示WHIRL,能表示各种高级控制流结构,但不能显式的提供数据流信息.本文基于WHIRL对SSA(Static Single Assignment)进行扩展,提出了一个新的优化结构WHIRL SSA.WHIRL SSA通过将SSA信息标注在WHIRL节点上,显式的为数据流分析提供使用-定义(UD)信息.相比于传统的数据流信息构建方法,WHIRL SSA提供了更精确、有效的数据流信息.本文讨论了WHIRL SSA的设计与实现和基于WHIRL SSA的优化.展开更多
文摘The noises of remote sensing images, caused by imaging system and ground environment, negatively affect the accuracy and efficiency in extracting forest information from remote sensing images. The denoising is critical for image classifications for forest areas. The objective of this research is to assess the effectiveness of currently used spatial filtering methods for extracting with forest information related from Landsat 5 TM images. Five spatial filtering methods including low-pass filter, median filter, mean filter, sigma filter and enhanced self-adaptive filter were examined. A set of evaluation indices was designed to assess the ability of each denoising method for flatness, edge/boundary retention and enhancement. Based on the designed evaluation indices and visual assessment, it was found that sigma filter （D=1） and enhanced self-adaptive filter were the most effective denoising methods in classifying TM images for forest areas.
