LAND COVER CLASSIFICATION USING REMOTE SENSING IMAGES AND LIDAR DATA

This study proposes a land cover classification method using remote sensing images and LiDAR data. In this method, deep learning and conditional random fields (CRF) optimization is applied for accurately classifying land covers. To address the issue that pixel-based deep learning methods are difficult to capture the precise outline of ground objects, we combine deep feature learning strategy with image objects for accurately interpreting remote sensing images. Context information revealing relationships between image objects are explored for optimizing the classification result by using object-based CRF, where height information derived from LiDAR data is considered. Vaihingen dataset is used to validate the proposed method and an overall classification accuracy of 92.4% is achieved.