The Research On Hyperspectral Band Selection Algorithm Based On Neighborhood Rough Set

Hyperspectral imagery(HSI)is known as its high spectral resolution,which can provide us with rich spectral information and has extensive application value.HSI has a number of bands,a huge amount of data and redundant information.These characteristics bring it certain difficulty in classification and identification.Therefore,it is necessary to explore a method for processing and analyzing data fast and utilizing information effectively.Band selection of hyperspectral data means to select a subset which contains important bands.The subset can retain useful information entirely,and can reduce the dimension greatly.Rough sets theory is a new mathematic approach to process the uncertain and vague data.It can reduce the data and obtain the core knowledge of data without loss of effective information.From the perspective of attribute reduction in rough sets theory,this study explores the band selection methods.The main contents are as follows:(1)The hyperspectral band selection method based on variable precision neighborhood rough set is put forward.By fusing the methods of attribute reduction in neighborhood rough set to band selection,this method enhances the ability to adapt to the noise data by introducing a variable precision factor β,that is allowing the approximation error of the upper and lower approximation.The selection of neighborhood radius δ and a variable precision factor β is the important factor of algorithm.The different δ and β may produce different attribute importance,and affect the attribute reduction subsets ultimately.This paper studies the effect of δ and β on the size of subset and classification accuracy,and determines the scope of δ and β through the experiment,and searches the optimal band subsets.(2)The hyperspectral band selection method based on consistency-measure of neighborhood rough set is proposed.Dependence measure does not take into account the samples in decision boundary.Concerning this issue,the consistency is introduced to neighborhood rough set.The algorithm selects band subsets based on neighborhood decision error minimization criterion for removing redundant band.And it can select appropriate size of subset in the full range of neighborhood,and has good adaptability and insensitivity to the change of neighborhood.For over-fitting problem of hyperspectral band selection algorithm,pre-pruning strategy and post-pruning strategy are proposed.The sizes of subset and classification performance of the two strategies are compared through the experiments.The result shows that the post-pruning strategy which combines the Filter method with the Wrapper method is more effective,and can obtain satisfactory classification performance.(3)The hyperspectral band selection method based on neighborhood mutual information is proposed.From the viewpoint of information view,entropy is introduced into the attribute reduction.The proposed algorithm regards the change value of neighborhood mutual information when add a band to the subset as the importance measure of the band.The attribute reduction algorithm based on information view is the complementation of that based on algebraic view,and can select the attributes which the algorithm based on algebraic view is unable to select.(4)The maximum relevance minimum redundancy(MRMR)band selection based on neighborhood rough set is proposed.Based on the neighborhood mutual information,redundancy among bands is considered fully,and the relevance between bands and categories and the redundancy among bands are combined.Two measures MRMR difference and MRMR quotient are defined.By the algorithm the optimal band subsets can be obtained,and satisfactory classification performance can achieved.In term of the stability,MRMRQ algorithm and MRMRD algorithm are better than other algorithms.(5)In addition to the classification performance,the paper provides another important indicator for measuring the performance of band selection algorithm-the stability.For the same hyperspectral datasets,several band subsets with the same or similar classification performance can be got.Selecting Jaccard coefficient as stability measure,the paper researches how overlap rate of sample subset,disturbance changes of sample subset,the size of sample subsets,and the size of neighborhood effect on the stability of the algorithm.Finally,the paper puts forward the comprehensive evaluation function which can take into account classification performance,stability,and the size of band subsets.According to actual condition,the parameters can be set flexibly to adjust the importance of classification performance,stability,and the subset size.The sensitivity of algorithms to noise and training samples is analysed,and the applicability of the algorithms is discussed.