A new semantic segmentation model for remote sensing images

Semantic segmentation for remote sensing images is a critical process in the workflow of object-based image analysis. Recently, convolutional neural networks(CNNs) are powerful visual models that yield hierarchies of features. In this paper, we propose a deep convolutional encoder-decoder model for remote sensing images segmentation. Specifically, we rely on the encoder network to extract the high-level semantic feature of ultra-high resolution images and the decoder network is employed to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise labeling. Also the fully connected conditional random field (CRF) is integrated into the model so that the network can be trained end-to-end. Experiments on the Vaihingen dataset demonstrate that our model can make promising performance.