Semantic pixel labelling in remote sensing images using a deep convolutional encoder-decoder model

In this letter, we propose a deep convolutional encoder-decoder model for remote sensing images semantic pixel labelling. Specifically, the encoder network is employed to extract the high-level semantic feature of hyperspectral images and the decoder network is employed to map the low resolution feature maps to full input resolution feature maps for pixel-wise labelling. Different from traditional convolutional layers we use a 'dilated convolution' which effectively enlarge the receptive field of filters in order to incorporate more context information. Also the fully connected conditional random field (CRF) is integrated into the model so that the network can be trained end-to-end. CRF can effectively improve the localization performance. Experiments on the Vaihingen and Potsdam dataset demonstrate that our model can make promising performance.