Woodland Detection Using Most-Sure Strategy To Fuse Segmentation Results Of Deep Learning

For obtaining information about ecosystem resource, GF-1 satellite was launched on April 26, 2013, which is the first satellite of the China's High-Resolution Earth Observation System. After obtaining some of the remote sensing images from GF-1, we selected WFV(wide field vision) images and detected the woodland to separate it from other geography types in the images. First, WFV images were clipped and labeled, then two deep learning models, POI-Net and Deep-UNet were used for training. We fused the prediction matrixes of deep learning networks using proposed "Most-sure strategy". The results show that our method can effectively improve the accuracy of woodland detection and segmentation results are outstanding. In addition, the proposed framework can also detect woodland in images returned by GF-6 satellite.