Water Body Extraction From Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model
Abstract: Water body extraction (WBE) has attracted considerable attention in the field of remote sensing image analysis. Herein, we present an enhanced deep convolutional encoder-decoder (DCED) network (or Deep U-Net) specifically tailored to WBE from remote sensing images by applying superpixel segmentation and conditional random fields (CRFs). First, we preclassify the entire remote sensing image into the water and nonwater areas via Deep U-Net, using the results of class membership probabilities as the unary potential in the CRF model. The pairwise potential of CRF is defined by a linear combination of Gaussian kernels, which forms a fully connected neighbor structure. Next, regional restriction is incorporated into the approach to enhance the consistency of the connected area. We use the simple linear iterative clustering algorithm to generate superpixels and correct the binary classification results by calculating their average posterior probabilities. Finally, a highly efficient approximate inference algorithm, mean-field inference,