Analysis of Rock Mass Weathering Grade Using Image Processing Technique

Abstract

Image processing techniques refer to the process of converting an image into a digital format and then performing various operations on it to extract useful information. In this study, image processing technique has been used to categorize rock masses according to its weathering grades. The pixel values of the sample images were in the form of RGB color space before being converted to CIELAB color space. The conversion uses ° as the illuminant. The value in CIELAB color space represents the green-red opponent colors, with negative values for green and positive values for red. In contrast, the value represents the blue-yellow opponents with negative values for blue and positive values for yellow. From the values of  and of the samples,  clustering was used to classify the samples. This method will group the  and  values into seven clusters according to the closest distance between the values and the centroids. The proposed study can differentiate between the rock mass and rejected clusters containing plants and painted numbers on the rock. The painted number is placed in a rejected cluster due to the inability to determine the exact color of the rock, thereby impacting the data accuracy. The results have been discussed, and the rock masses have been categorized based on weathering grade. Several limitations have been identified, such as the presence of shadows in the sample images and the lack of arrangement of outcome images according to their a* and b* values. This research has also been validated and compared with previous studies. The JudGeo software utilized in prior research required human input to manually estimate suitable a* and b* values, whereas the proposed method automatically computes these values during color space conversion of the sample. Additionally, the proposed method can calculate the percentage of each cluster, facilitating the classification of rock mass into its respective weathering grade.Image processing techniques refer to the process of converting an image into a digital format and then performing various operations on it to extract useful information. In this study, image processing technique has been used to categorize rock masses according to its weathering grades. The pixel values of the sample images were in the form of RGB color space before being converted to CIELAB color space. The conversion uses ° as the illuminant. The value in CIELAB color space represents the green-red opponent colors, with negative values for green and positive values for red. In contrast, the value represents the blue-yellow opponents with negative values for blue and positive values for yellow. From the values of  and of the samples,  clustering was used to classify the samples. This method will group the  and  values into seven clusters according to the closest distance between the values and the centroids. The proposed study can differentiate between the rock mass and rejected clusters containing plants and painted numbers on the rock. The painted number is placed in a rejected cluster due to the inability to determine the exact color of the rock, thereby impacting the data accuracy. The results have been discussed, and the rock masses have been categorized based on weathering grade. Several limitations have been identified, such as the presence of shadows in the sample images and the lack of arrangement of outcome images according to their a* and b* values. This research has also been validated and compared with previous studies. The JudGeo software utilized in prior research required human input to manually estimate suitable a* and b* values, whereas the proposed method automatically computes these values during color space conversion of the sample. Additionally, the proposed method can calculate the percentage of each cluster, facilitating the classification of rock mass into its respective weathering grade.