A comparative study of the denoising methods of Thematic Mapper images for forest areas
| Autor: | Zheng-yong Zhao, Lihai Wang |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Spatial filter
Computer science business.industry Flatness (systems theory) Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Forestry Pattern recognition Filter (video) Thematic Mapper Computer Science::Computer Vision and Pattern Recognition Visual assessment Median filter Artificial intelligence Enhanced Data Rates for GSM Evolution business |
| Zdroj: | Journal of Forestry Research. 18:123-127 |
| ISSN: | 1993-0607 1007-662X |
| DOI: | 10.1007/s11676-007-0024-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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