Multitemporal Classification Under Label Noise Based on Outdated Maps

Abstract

Supervised classification of remotely sensed images is a classical method for change detection. The task requires training data in the form of image data with known class labels. If the training labels are acquired from an outdated map, the classifier must cope with errors in the training labels. These errors (label noise) typically occur in clusters in object space, because they are caused by land cover changes over time. In this paper we adapt a label noise tolerant training technique for classification, so that the fact that changes affect larger clusters of pixels is considered. We also integrate the existing map into an iterative classification procedure to act as a priori in regions which are likely to contain changes. Additionally we expand the model for multitemporal data, making it applicable for time series. Our experiments are based on four test areas, including a multitemporal example. Our results show that this method helps to distinguish between real changes over time and false detections caused by misclassification and thus improve the accuracy of the classification results.