Crop classification at subfield level using Rapid Eye time series and graph theory algorithms

摘要

Accurate information about land use patterns is crucial for a sustainable and economical use of water in agricultural systems. Water demand estimation, yield modeling and agrarian policy are only a few applications addressed by land use classifications based on remote sensing imagery. In Central Asia, where fields are traditionally large and state order crops dominate the area, small units of fields are often separated for the additional cultivation of income crops for the farmers. Traditional object based land use classifications on multi-temporal satellite imagery using field boundaries show low classification accuracies on these separated fields, expressed by a high uncertainty of the final class labels. Although segmentation of smaller subfields was shown to be suitable for improving the classification result, the extraction of subfields is still a time-consuming and error-prone process. In this study, energy based Graph-Cut segmentation technique is used to enhance the segmentation process and finally to improve the classification result. The interactive segmentation technique was successfully adopted from bio-medical image analysis to fit remote sensing imagery in the spatial and in the temporal domain. A set of rules was developed to perform the image segmentation procedure on pixels of single satellite datasets and on objects representing time series of a vegetation index. An ensemble classifier based on Random Forest and Support Vector Machines was used to receive information about classification uncertainty before and after applying the segmentation. It is demonstrated that subfield extraction based on Graph Cuts outperforms traditional image segmentation approaches in simplicity and reduces the risk of under- and over-segmentation significantly. Classification uncertainty decreased using the derived subfields as object boundaries instead of original field boundaries. The segmentation technique performs well on several multi-temporal satellite images without changing parameters and may be used to refine object based land use classifications to subfield level.