摘要
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.
遥感图像会因成像系统和地理环境而产生噪音,这些噪音将会影响从TM图像中提取森林信息的精确性和有效性。消除噪音对图像的分类十分重要。本研究的目的是评估应用Landsat 5 TM图像提取森林相关信息时,目前所使用的空间滤波处理方法的有效性。对低通滤波、中值滤波、均值滤波、求和滤波、增强型自适应滤波五种空间滤波方法做以检验。通过设计一系列的评估指数,分析每种噪音消除方法的平滑能力、边界保持和增强能力。基于所设计的评价指数和图片对比表明,对林区土地利用和森林类型分类而言,求和滤波(D=1)和增强型自适应滤波是消除Landsat 5 TM图像噪音的最有效的方法。图1表2参29。
