Geologian tutkimuskeskus, Tutkimusraportti 198 – Geological Survey of Finland, Report of Investigation 198, 2013 Pentti Hölttä (ed.)
DEVELOPMENT OF A SEMI-AUTOMATED PROCESS FOR INTERPRETATION OF LITHOGEOCHEMICAL BEDROCK DATA USING GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS (GEOBIA) TECHNIQUES by Soile Aatos Geological Survey of Finland, P.O. Box 1237, FI-70211 Kuopio, Finland Email:
[email protected] The volume of geochemical data generated during geological, exploration and environmental mapping programs, is growing rapidly, in part due to advances in the range and quality of chemical analytical techniques and instrumentation, and in part due to increased computational capacity and efficiency in managing and delivering complex, spatially-oriented geochemical datasets. Object-based image analysis (OBIA) is a concept that facilitates the simultaneous interpretation of large amounts of image-oriented data e.g. dense image data at different scales, object height information and other types of thematic data. It represents a promising approach to optimizing workflows where manual input and processing may inhibit efficient analysis and interpretation of voluminous and multivariate image and feature data. Recent applications of OBIA to remote sensing have included integrated analysis of geographically bound raster domain imagery and vector domain feature data; this has now developed into a new research field known as Geographic Object-Based Image Analysis (GEOBIA). In this study, GEOBIA concepts and techniques have been combined with GIS spatial analytical tools to develop a more efficient, semi-automated image interpretation process using randomly sampled, lithogeochemical vector domain data. Emphasis was also given to developing an expert-driven data reduction procedure for accurately classifying probable source rock types based on as little geochemical data as possible. The software used for geographic object-based image analysis, classification and identification in this study were ESRI® ArcGIS™ (ArcGIS) and Trimble© eCognition Developer™ (eCognition). ArcGIS was used for preprocessing and interpolation of GIS point data to raster image sets for the GEOBIA modeling process. eCognition was used for object-based image segmentation of the original raster data, classification of image-based objects with a pattern recognition algorithm and export of vectorized object data for further use in the GIS environment. The resultant classified vector data sets were visualized in ArcGIS and exported as map image products. The original input data used in this study were derived from the Rock Geochemical Database of Finland (RGDB) compiled, and comprehensively doc8
Geologian tutkimuskeskus, Tutkimusraportti 198 – Geological Survey of Finland, Report of Investigation 198, 2013 Current Research: GTK Mineral Potential Workshop, Kuopio, May 2012
umented by Rasilainen et al. (2007, 2008). This dataset was chosen because it contains analytically consistent and well-documented lithogeochemical data representing a wide range of Finnish bedrock lithologies, as described. The RGDB results were stored in vector format in a GIS database containing bedrock analytical data; for this study, the whole-rock XRF data were used. The original XRF data matrix in the RGDB had dimensions of 6544 rows by 36 columns. For the purposes of modeling, the data were reduced to four major element oxide components, namely aluminium (Al2O3), potassium (K2O), silicon (SiO2) and magnesium (MgO), resulting in a matrix with dimensions of 6544 by 4. These four oxides were each chosen to correspond to conceptual rock type model domains representing four dominant Finnish rock type categories (Al2O3 = aluminous metasediments or schists, K2O = granitoids, SiO2 = siliceous metasediments or quartzites and MgO = mafic rocks). The four lithogeochemical rock type model domains were individually assigned to form four corresponding raster image colour bands in the CMYK colour domain (Al2O3 [min, max] > C=cyan [0, 255], K2O [min,max] > M=magenta [0,255], SiO2 [min,max] > Y=yellow [0,255] and MgO [min,max] > K=black [0,255]), by interpolating the total element oxide concentrations with ArcGIS Spatial Analyst Tools. The colour selection was defined so as to correspond as closely as possible to the spectral features assigned to various rock types on the traditional Finnish bedrock geological map, notably schists (blue), granitoids (red), quartzites (yellow) and basic rocks (dark hues) to help to make classification decisions and to outline the classification results later in the eCognition software platform. The classified C, M, Y and K raster image visualizations were combined in ArcGIS to form a single CMYK raster image data set. In eCognition, the CMYK image data set was processed further as three individual RGB color bands R=red [0,255], G= green [0,255] and B= blue [0,255] and segmented to form image objects from pixel data. Image objects were classified according to their naturally occurring spectral features with a sample-based pattern recognition algorithm. The neighboring objects having the same class were then processed by merging to form larger image objects providing a geographical representation of the interpreted chemically equivalent rock type classes. The interpreted and classified lithogeochemical image object raster features were exported in vector format to ArcGIS for compilation of a geochemical GIS map presentation and for further optimization in the visualization of map products. The GIS and GEOBIA techniques and tools together form a very promising, powerful and resource efficient way of interpreting large amounts of geochemical data based on the use of only a few image model parameters. At present, there seem to be no obvious technical limitations in the hardware, software, or computational algorithms for ongoing GEOBIA process development. Future developments towards a more standardized geochemical GEOBIA interpretation process could include optimizing the image colour domain, choosing a more suitable interpolation method for sparse and randomly sampled geochemical GIS data, and finding an efficient image geo-referencing procedure within the overall workflow. The GEOBIA rule sets and procedures developed in this study can also be applied to other types of geochemical data. The integration of geochemical GIS data with other types of geodata in 2D and ultimately in 3D would be another area of potential interest in applying GEOBIA to geological interpretation.
9
Geologian tutkimuskeskus, Tutkimusraportti 198 – Geological Survey of Finland, Report of Investigation 198, 2013 Pentti Hölttä (ed.)
REFERENCES Rasilainen, K., Lahtinen, R. & Bornhorst, T. J. 2007. The Rock Geochemical Database of Finland Manual. Geological Survey of Finland, Report of Investigation 164. 38 p. Rasilainen, K., Lahtinen, R. & Bornhorst, T. J. 2008. Chemical characteristics of Finnish bedrock – 1:1 000 000 scale bedrock map units. Geological Survey of Finland, Report of Investigation 171. 94 p.
10
