Textural Features for HiRISE Image Classification Lauren Hunkins 1,2, Mario Parente 1,3, Janice Bishop 1,2 1 SETI Institute; 2 NASA Ames; 3 Stanford University National Aeronautics and Space Administration Texture Samples 1. Inverted Polygons 2. Small Polygons 3. Craquelure 4. Dunes5. Caprock Textural Features We are comparing two different methods for obtaining the textural features of our images, GTSDM's and GLRLM's. The GTSDM's are based on statistics which summarize the relative frequency distribution, describing how often one gray tone will appear in a specified spatial relationship to another gray tone. This approach assumes that the texture-context information is contained in the overall spatial relationship of gray tones in the image. With this method, it is easy to discriminate between coarse and fine textures. The GLRLM's extract the information of an image from it's gray level runs. A run is consecutive pixels of the same gray value, in a given direction. This method makes it possible to discriminate between highly directional features. Conclusion We hope to be able to combine the numerical measures calculated from the GTSDM's and GLRLM's to use in classifying images taken by HiRISE. By creating a classification algorithm using the data set created in this study, textures can be classified autonomously. Acknowledgements This work was made possible by generous grants made by NASA’s Motivated Undergraduates in Science and Technology and the Hispanic College Fund. Numerical Measures In this study, we used 8 numerical measures (i.e. mean, variance, correlation...) to extract data from the GTSDM's and 11 numerical measure (i.e. short run emphasis, gray-level nonuniformity, run percentage...) to extract data from the GLRLM's. The extracted data provide distinct numerical values that can be used to discriminate different textures. Graphs Data available on Wednesday Abstract We determine the best method for analysis of texture on Mars in thisstudy by comparing the numerical measures derived from the use of gray-tone spatial-dependence matrices (GTSDM’s) and gray level run length matrices (GLRLM’s) on images obtained by the High Resolution Imaging Science Experiment (HiRISE) camera located on the Mars Reconnaissance Orbiter (MRO). We focus our attentions on smectite regions as identified by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) also on MRO. The GTSDM’s and GLRLM’s are applied to regions of the image to derive the textural features in that region. Numerical texture measures are obtained using functions defined by Haralick (1973) and Galloway (1974). These numerical measures are compared across regions to determine geomorphological differences. Experiments for the discrimination of textures on Mars including dunes, polygons, inverted polygons, caprock, and craquelure are being conducted. Expected results include the ability to discriminate among these features consistent with discrete geomorphological units and classify these regions on images captured by the HiRISE camera. References 1. 2.