Lithostratigraphic Classification Method Combining Optimal Texture Window Size Selection and Test Sample Purification Using Landsat 8 OLI Data

Abstract

Gray Level Co-Occurrence Matrix (GLCM), as a measure of spatial features has been used as supplemental information to improve image classification accuracy for lithological recognition. Window size is an important parameter for texture extraction, which will affect the extracted texture results. Besides, the existence of mixed pixels in image usually causes errors in test samples, which significantly influences the credibility of accuracy assessment. Thus, this paper proposes a lithological classification method combined with optimal texture window size selection and test sample purification. Firstly, optimal window size pre-estimated based on semivariogram was used to calculated GLCM texture of image. Secondly, based on multidimensional textural and spectral features, a support vector machine (SVM) classifier was employed to classify the image. Thirdly, using the proposed sample purification method and textural features of image, sample purification rules were created based on attribute coherence to remove the test sample points that conflicted with the rules. Finally, the validity of the semivariogram-based texture extraction window selection was verified by classifications based on Angular Second Moment (ASM) of different window sizes combined with spectral features. Also, the accuracies between different combinations of classifications were assessed by test samples with and without sample purification. Experimental results show that the pre-estimated texture window size can guarantee a classification result with high classification accuracy for lithological classification. The results also demonstrated that the accuracy of lithological classification based on spectral features and ASM textural features was the highest. The overall lithological classification accuracy and kappa value, without sample purification selected by stratified sampling, were respectively 87.4% and 0.84, however those with sample purification were respectively 88.01% and 0.85. The results show that the proposed method is capable of yielding more reliable lithostratigraphic identification.

Keywords

• lithostratigraphic classification
• glcm
• accuracy assessment
• sample purification
• semivariogram