TY - JFULL AU - Tossaporn Kachanubal and Somkait Udomhunsakul PY - 2008/4/ TI - Rock Textures Classification Based on Textural and Spectral Features T2 - International Journal of Geotechnical and Geological Engineering SP - 657 EP - 664 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/8816 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 15, 2008 N2 - In this paper, we proposed a method to classify each type of natural rock texture. Our goal is to classify 26 classes of rock textures. First, we extract five features of each class by using principle component analysis combining with the use of applied spatial frequency measurement. Next, the effective node number of neural network was tested. We used the most effective neural network in classification process. The results from this system yield quite high in recognition rate. It is shown that high recognition rate can be achieved in separation of 26 stone classes. ER -