Spatial-contextual supervised classifiers explored: a challenging example of lithostratigraphy classification
Cracknell, MJ and Reading, AM, Spatial-contextual supervised classifiers explored: a challenging example of lithostratigraphy classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, (3) pp. 1371-1384. ISSN 1939-1404 (2015) [Refereed Article]
Spatial-contextual classifiers exploit characteristics of spatially referenced data and account for random noise that contributes to spatially inconsistent classifications. In contrast, standard global classifiers treat inputs as statistically independent and identically distributed. Spatial-contextual classifiers have the potential to improve visualization, analysis, and interpretation: fundamental requirements for the subsequent use of classifications representing spatially varying phenomena. We evaluate random forests (RF) and support vector machine (SVM) spatial-contextual classifiers with respect to a challenging lithostratigraphy classification problem. Spatial-contextual classifiers are divided into three categories aligned with the supervised classification work flow: 1) data preprocessing-transformation of input variables using focal operators; 2) classifier training-using proximal training samples to train multiple localized classifiers; and 3) postregularization (PR)-reclassification of outputs. We introduce new variants of spatial-contextual classifier that employ self-organizing maps to segment the spatial domain. Segments are used to train multiple localized classifiers from k neighboring training instances and to represent spatial structures that assist PR. Our experimental results, reported as mean (n = 10) overall accuracy ±95% confidence intervals, indicate that focal operators (RF 0.754 ±0.010, SVM 0.683 ±0.010) and PR majority filters (RF 0.705 ±0.010, SVM 0.607 ±0.010 for 11 × 11 neighborhoods) generate significantly more accurate classifications than standard global classifiers (RF 0.625 ±0.011, SVM 0.581 ±0.011). Thin and discontinuous lithostratigraphic units were best resolved using non-preprocessed variables, and segmentation coupled with postregularized RF classifications (0.652 ±0.011). These methods may be used to improve the accuracy of classifications across a wid- variety of spatial modeling applications.