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| ==== Decision tree regression for soft classification of remote sensing data ==== | ==== Decision tree regression for soft classification of remote sensing data ==== | ||
| - | //Xu, M., Watanachaturaporn, P., Varshney, P. Arora, M.// | + | //Remote Sensing of Environment, 2005.// |
| - | //Remote Sensing of Environment// | + | //Xu, M., Watanachaturaporn, P., Varshney, P. Arora, M.// |
| This work proposes a decision tree with soft thresholds, which decompose the pixel into its class constituents in the form of class proportions. The outputs from soft classification provide a set of fraction images that represent proportion of classes for each pixel. The use of decision trees is justified because remote sensing data often not follow Gaussian distribution, an assumption made by several classifiers. | This work proposes a decision tree with soft thresholds, which decompose the pixel into its class constituents in the form of class proportions. The outputs from soft classification provide a set of fraction images that represent proportion of classes for each pixel. The use of decision trees is justified because remote sensing data often not follow Gaussian distribution, an assumption made by several classifiers. | ||
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| Another suggestion is to use "feature extraction", which means creating new features by applying combinations on already existent features. However, "although extracted features have higher discrimination power, their physical meanings were hard to explain, which lowered the interpretability of classification trees". | Another suggestion is to use "feature extraction", which means creating new features by applying combinations on already existent features. However, "although extracted features have higher discrimination power, their physical meanings were hard to explain, which lowered the interpretability of classification trees". | ||
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| + | ===== Image Processing ===== | ||
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| + | ==== High spatial resolution spectral mixture analysis of urban reflectance ==== | ||
| + | //Remote Sensing of Environment, 2003.// | ||
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| + | //Small, C.// | ||
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| + | The author explores the use of IKONOS imagery for intraurban classification. Medium spatial resolution sensors, such as TM and SPOT present abundance of mixed pixels. However, mixed pixels are problematic for statistical classification methods because most algorithms are based on the assumption of spectral homogeneity at pixel scale within a particular class of land cover, but urban areas provide examples of spectrally diverse, scale-dependent thematic classes containing large numbers of pixels that are spectrally indistinguishable from other land cover classes. The work proposes the use of spatial autocorrelation to quantify the characteristic scale lengths of urban reflectance within and among different cities. The question of the paper is if the consistency of the spectral mixing space for a urban areas can be provided by a simple three-component linear mixture model, to characterize the urban reflectance. Concluding, it is argued that spectral mixture analysis is preferable to "hard classification" for many applications because it accommodates the preponderance of mixed pixels observed in almost all multispectral imagery. | ||
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