Analysis of Multitemporal Satellite Image Pattern Recognition using Haralick Method to Identify Rice Plant Growth Phase
This study focused on the manipulation of multitemporal satellite image data namely Landsat 8 of rice plant growth phase identification to determine pattern group or category based on the features possessed by the image pattern based on the growth phase of rice plant in which the initial process of pattern recognition was through the feature extraction on the image by using gray level co occurrence matrix (GLCM) and Haralick methods which were then used as inputs of pattern formation. Feature extraction was the first step in determining the parameters as the interpretation of image texture analysis to determine the pattern contained in the image. Based on the analysis results of the comparison of each parameter value in this study, it was found that the results of analysis using angle parameter with the identification of all angles (All) in the feature extraction and the parameter of color intensity, namely Red, Green and Blue (RGB) could be used for object pattern recognition in multitemporal image such as Landsat, the use of these parameters was to obtain more varied information, so that the object pattern information in the image was easier to interpret. Pattern recognition is to determine the pattern group or category based on the features possessed by the pattern, the purpose is to distinguish between one object with another object based on the pattern owned by the object in the image. Satellite image is the image data generated from remote sensing technology, satellite image extraction process is needed to interpret the pattern of an object in an image, this is performed so that information from the object being observed can be estimated accurately. Information generated from satellite image data is highly dependent on the image processing that is performed well either by using good model or method to get the results that have a high degree of accuracy.