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- W2591958324 abstract "This chapter proposes a new method of high resolution optical remote sensing image shadow extraction algorithm based on Gram-Schmidt orthogonalization addressing the issues of current shadow extraction algorithm’s complex modelling and low efficiency in computation, the basic idea of which is that the inner product of different orthogonal vectors equals to zero. The basic steps are as follow: First, the transform of the image from RGB space to Lab space is conducted based on the principle of human visual system, then the values of L/a/b shall all be confined in the range of [0, 255], which shall be stored in the three channels of RGB respectively for the following process. Colour inversion of the shadow is necessary due to its cool-colour darkness on the image and the inverted warm colour is convenient for the follow process. Take samples from the colour inverted image and the three corresponding channel values of image pixels will be used as feature vectors. Then obtain the mean values of samples of the same class for each channel, which will be processed as the feature vector for that class. Finally, the Gram-Schmidt orthogonalization computation shall be conducted for the eigenvector of each class to obtain the corresponding orthogonalization vector, and then use the corresponding orthogonalization vector of shadow characteristic vector as weight to enhance the colour feature vectors of the shadow meanwhile reducing the colour feature vectors of non-shadowed in order to extract the shadow from the image. Comparing with the current shadow extraction algorithm, the effectiveness of the method proposed in the chapter is verified." @default.
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- W2591958324 date "2017-01-01" @default.
- W2591958324 modified "2023-09-23" @default.
- W2591958324 title "Shadow Extraction from High-Resolution Remote Sensing Images Based on Gram-Schmidt Orthogonalization in Lab Space" @default.
- W2591958324 cites W2023151535 @default.
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- W2591958324 doi "https://doi.org/10.1007/978-3-319-49184-4_32" @default.
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