Influence of different data types and dimension reduction on the recognition accuracy of travertine hyperspectral images
Travertine is a kind of travertine carbonate precipitate that is generated when huge quantities of carbon dioxide are released from the surface of the earth.The formation of a large-scale landscape from this type of precipitate often takes a considerable length of time.Therefore,the travertine landscape may be used as a significant carrier for the study of crustal movement,paleoclimate,and other geological settings.Furthermore,the large-scale travertine landscape,which is considered as a natural heritage,is significant for conservation with a high tourist value.This study focuses on the Huanglong Scenic Area in China,which is recognized as a global natural heritage site by the United Nations Educational,Scientific,and Cultural Organization(UNESCO).This area is renowned for its expansive surface travertine landscapes that include a wide variety of distinctive formations and vibrant colors.The travertine in Huanglong,on the other hand,has been experiencing major deterioration in recent years,such as blackening and algal erosion.Therefore,the recognition and monitoring of travertine is urgent.This study proposes a method of recognizing travertine based on hyperspectral reflectance data in order to facilitate the protection and restoration of travertine resources.This method can be used to effectively tackle the problems brought about by traditional field surveys that are time-consuming,labor-intensive and likely destructive to travertine landscapes.This study was conducted in the following procedure.Four types of data were taken as classification objects,that is,original data and other three types of data that were converted respectively by multiple scattering,first-order derivative and second-order inverse for the original data.Then,these four types of data were respectively reduced to their corresponding dimensions by Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA),according to the magnitude of the cumulative variance of the data.Finally,for the classification of these four types of data after dimension reduction,they were respectively put into four kinds of classifiers,namely,Support Vector Machines(SVM),Random Forests(RF),BP Neural Networks,and Convolutional Neural Networks(CNN).Overall Classification Accuracy(OA)was used as an evaluation index.In addition,Particle Swarm Algorithm(PSO)was used to optimize the penalty coefficient C and the Gammer parameter values of SVM.Afterward,the optimized SVM was applied to develop a recognition model of classification.Moreover,three indicators,namely,F1-Score,Kappa coefficient,and OA were utilized to assess the performance of SVM recognition model.In terms of the data type and the method of dimension reduction,the classification results of the recognition model established in this study were studied.In the aspect of the method of data dimension reduction,it was discovered that dimension reduction of the original data by PCA was superior to that by LDA.Furthermore,the classification model of the original data by PCA dimension reduction was generally more accurate than that by LDA.With regard to the type of data,the mean value of the model accuracy with MSC data as input was 88％,which was the second largest among the four types of data,only 0.1％lower than the first one.However,its variance and standard deviation were 0.043 and 0.042,respectively,much smaller than those of the models with the other three types of data,which indicated that the recognition model with MSC data was much more stable.Finally,the SVM classification model that was optimized by PSO demonstrated its outstanding performance when evaluated from the three performance indexes:Fl-score,kappa coefficient,and OA.In general,this performance is superior to that of the unoptimized SVM recognition model,with the SD-PCA-PSO-SVM model the best performance among the three.Values of F1-Score,Kappa and OA of the classification results by optimized SVM were 0.93,0.92,and 0.98,respectively.In conclusion,it is easier for the unoptimized classifier to acquire a high-precision recognition model,if the MSC data or the original data processed by PCA dimension reduction were selected as the input in the recognition of travertine.Additionally,selecting an appropriate theory to optimize the model can also improve the recognition performance of the model.
travertinehyperspectral imagedata dimension reduction and transformationparticle swarm optimizationsupport vector machine
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