Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example

With the constant proliferation of computational power, our ability to develop hybrid classifiers has improved. Hybrid classifiers integrate results from multiple algorithms and often improve classification accuracy. In this paper, a hybrid classification framework was used to evaluate two research hypotheses: i) can manipulated results from prior classifiers (“intermediate inputs” (IIs)) improve classification accuracy in subsequent classification steps. and ii) is there an optimal dataset proportion for creation and usage of intermediate inputs. These additional intermediate inputs were based on spatial and texture statistics calculated on a partially classified image. The implementation of intermediate inputs on an impervious surface classification task using a 2001 Landsat ETM+ image from central New York was demonstrated. The results suggested that there was an average accuracy improvement of 3.6% (maximum 6.6%) by using intermediate inputs. These improvements were proved statistically significant by a Z-test and tended to increase as classification difficulty increased. The experiments in this paper also showed that there was an optimal point that balanced the number of pixels and pixel classification accuracy from prior steps used to produce intermediate inputs. Additionally, some traditional problems such as separation of impervious surfaces and soil were successfully tackled through intermediate inputs. The concept of the intermediate inputs may easily apply to other sensors and/or ground features.