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- W2380491027 abstract "Aiming at overcoming the limitations in extracting impervious surface by traditional methods,two non-linear spectral mixture models,Mixture Tuned Matched Filtering (MTMF) and Multi-Layer Perceptron(MLP) neural network,are used to decompose all pixels to the four fraction images representing the abundance of four endmembers.In these models,MTMF performs a partial unmixing by only finding the abundance of a single,user-defined endmember,by maximizing the response of the endmember of interest and minimizing the response of the composite unknown background.The MLP is a hierarchical structure of several perceptrons,and capable of learning a rich variety of nonlinear decision surfaces.The Maximum Noise Fraction(MNF) is used to transform the six bands of TM image into a newfeature space and the first three components accounting for the majority (more than 90 %) of total information content are used to endmember extraction.After that,the Pure Pixel Index(PPI) is used to select pure pixels.The N-dimensional visualizer is used for assisting selection of four endmembers :vegetation,high-albedo objects,low-albedo objects and soil.The fraction images are derived to represent the abundance of the above four endmember.Impervious surface is esti mated by analyzing high-albedo and low-albedo fraction images.QuickBird multi-spectral image is used to evaluate the accuracy of impervious surface extraction by different methods.Experimental results indicate that the accuracy of artificial neural networkis higher than others,which means non-linear spectral mixture models is also effective to impervious area extraction,even better thanlinear models." @default.
- W2380491027 created "2016-06-24" @default.
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- W2380491027 date "2011-01-01" @default.
- W2380491027 modified "2023-09-25" @default.
- W2380491027 title "Urban Impervious Surface Extraction from Remote Sensing Image Based on Nonlinear Spectral Mixture Model" @default.
- W2380491027 hasPublicationYear "2011" @default.
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