| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系所 === 102 === In recent years, with the progress in remote sensing technologies, the numbers of data and dimension are increased in remote sensing imagery. For solve the huge data and high dimension computational complexity problem in remote sensing imagery can use band selection methods to reduced dimension and avoid Hughes Phenomena because of increased quantity of bands.
Some scholars proposed many kinds of algorithms for band selection to reduce dimensionality, but those algorithms were inefficient and led to the dimensionality reduction effects couldn’t be significantly. Therefore, in this paper, using a band selection algorithm based on particle swarm optimization (PSO) combine with correlation coefficients matrix (C.C Matrix) to cluster the highly correlated bands together and to obtain greedy modular eigenspace (GME) and represented as analytic hierarchy process (AHP) module to observe the relationship with modular hierarchically. To combine AHP with impurity function class overlapping (IFCO) to calculate high correlation bands weights. Finally, according to the bands weighting scores statistics result to select representative bands for obtaining greatly dimensionality reduction effect.
The effectiveness of the proposed method is evaluated by MASTER and AVIRIS remote sensing images for testing the variance and correlation of dimension reduction rate and classification accuracy. The experimental results show the dimension reduction rate is 90.91% and classification accuracy is 98.44% in Au-Ku; the dimension reduction rate is 85.00% and classification accuracy is 96.48% in NTC.
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