A new approach for automated and transferable Land Cover Classification methods using modular sampling data management, training, and image classification (2009)

Abstract Within the framework of the WISDOM project - a German-Vietnamese research initiative focussing on the development of a Water Related Information System for the Mekong Delta - derivation of high precision Land Cover and Land Use maps is an important key factor. These products are required at varying spatial scales for analyses such as vulnerability studies, hydrological modelling, water quality assessments and change detection studies. The study area of the project comprises – next to the Mekong basin and Delta as a whole – three districts of the Mekong Delta (Vietnam) with predominant cultivation of rice crop. This paper presents the development of an automated land cover classification processor for mono-temporal data sets transferable to different geometric resolutions and neighbouring spatial regions. The special focus is on automatisation of classification of stable land cover classes to include into a processing chain into the WISDOM Information System. A scheme had to be developed, which allows automated classification with as little analyst bias as possible, allowing for repeated mapping. The development of a hierarchical classification scheme was based on the FAO Land Cover Classification System aiming to get transferable classification results in a specific number of levels. To satisfy the needs of the potential users of the Information System two additional levels were added to represent land cover and land use classes taken from classification systems defined by Vietnamese ministries. Furthermore, a sampling database has been established to manage large amounts of sampling data to be utilized for training and validation of the subsequent classifier. So far, over one million sampling data points derived from different satellite images and field data measurements are managed. The sampling database stores the spectral information of the classes identified in the satellite image as well as additional features such as ratio- and indices values (NDVI, NDWI, etc.). As for the image classification the data mining software C5.0 has been utilized. Training and validation samples are retrieved automatically from the database. A preparatory statistical analysis of the training samples within C5.0 delineates the most probable features and -weightings for class separation. Subsequently, rule sets are generated and transformed into a decision tree. The classification of the satellite image is performed with an implementation of the C5.0 rules in Python language with reference to the probability values of the membership for every pixel. Finally, accuracy assessment is performed with the validation data taken from the sampling data base. The introduced approach has been applied to several high-resolution satellite images. Accuracy assessment of the classification results exhibited very high classification accuracies over all classes. The modular classification approach as setup with a sampling database, training of the classificator, and subsequent image classification appears to be robust in terms of accuracy and transferability. Furthermore, the given modular design allows for exchanging the applied classification methods, e.g. maximum likelihood estimation or support vector machines.