Increasing the accuracy of neural network classification using refined training data

Abstract

Image classification is a complex process affected by some uncertainties and decisions made by the researchers. The accuracy achieved by a supervised classification is largely dependent upon the training data provided by the analyst. The use of representative training data sets is of significant importance for the performance of all classification methods. However, this issue is more important for neural network classifiers since they take each sample into consideration in the training stage. The representativeness is related to the size and quality of the training data that are highly important in assessing the accuracy of the thematic maps derived from remotely sensed data. Quality analysis of training data helps to identify outlier and mixed pixels that can undermine the reliability and accuracy of a classification resulting from an incorrect class boundary definition. Training data selection can be thought of as an iterative process conducted to form a representative data set after some refinements. Unfortunately, in many applications the quality of the training data is not questioned, and the data set is directly employed in the training stage. In order to increase the representativeness of the training data, a two-stage approach is presented, and performance tests are conducted for a selected region. Multi-layer perceptron model trained with backpropagation learning algorithm is employed to classify major land cover/land use classes present in the study area, the city of Trabzon in Turkey. Results show that the use of representative training data can help the classifier to produce more accurate and reliable results. An improvement of several percent in classification accuracy can make significant effect on the quality of the classified image. Results also confirm the value of visualization tools for the assessment of training pixels through decision boundary analysis.