30 December 1994 Classification of multispectral imagery using wavelet transform and dynamic learning neural network
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Abstract
A recently developed dynamic learning neural network (DL) has been successfully applied to multispectral imagery classification and parameter inversion. For multispectral imagery classification, it is noises and edges such as streets in the urban area and ridges in the mountain area in an image that result in misclassification or unclassification which reduce the classificalion rate. At the image spectrum point of view, noises and edges are the high frequency components in an image. Therefore, edge detection and noise reduction can be done by extracting the high frequency parts from an image to improve the classification rale. Although both noises and edges are the high frequency components, edges represent some userul information while noises should be removed. Thus, edges and noiscs must be separated when the high frequency parts are extracted. The conventional edge detection or noise reduction melhods could not distinguish edges from noises. A new approach, Wavelet transform, is selected to fulfill this requirement. The edge detection and noise reduction pre-processing using Wavelet transform and image classification using dynamic learning neural network are presented in this paper. The experimental results indicate that it did improve the classification rate.1
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H. C. Chen, Yu-Chang Tzeng, "Classification of multispectral imagery using wavelet transform and dynamic learning neural network", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196715; https://doi.org/10.1117/12.196715
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