Developing a New Algorithm Based on Statistical Analysis of the Spectral Behavior of Features for Extracting Training Data Automatically for Classification of Remotely Sensed Images

Abstract

Automatic training data extraction approaches are new solutions for supervised classification. Most of the researches are aimed to develop automatic methods since conventional methods are time- and cost-consuming. Also, in conventional methods the results are impacted by the expertise of operators. On the other hand, factors such as size, number, distribution and homogeneity of training samples affect the overall accuracy. Therefore, examining the impact of these factors is time-consuming in traditional methods. Newly presented approaches have removed the problems of traditional methods although they have their own issues such as more detailed data in hyper-spectral images or heterogeneity inner different classes. These factors influence the accuracy of classification. In this paper, we study the behavior of raw data and extracted features separately, and then identify a stable pattern for automatic recognition of each class. The study is based on frequency distribution and statistical analysis of spectral behavior of ground features. Investigation on the results exposes that some classes have stable patterns and can easily be identified according to their behavior. The behavior of each class depends on selected bands and features, and can be regular or irregular. For instance, water class has low density distribution that makes it difficult to fit a distribution function, but it is distinguishable thorough unpredictable distribution function. In the other hand, urban class has regular pattern and an appropriate distribution can be fitted to the frequency of the features extracted from its sample dataset. Although an individual class may be recognized through several features simultaneously, using whole available features is time-consuming. So, just intrinsic combination of features should be used for classification. In the research, the behaviors of studied classes have been presented in different charts and diagrams, and ultimately, a new algorithm (in flowchart format) has been presented for intelligent sampling and training data extraction based on statistical analysis of spectral behavior of classes.