LAUSR

ABSTRACT Water-body image segmentation is a fundamental operation of many important applications, such as water resources allocation, ecological assessment, flood control, etc. Mainstream neural network based segmentation algorithms are still far from satisfactory for segmenting water-body; complex natural land and water boundaries can easily lead to inaccurate classification with little boundary detail preserved. To improve the performance of water-body segmentation, we propose a novel technique based on feature pyramid enhancement and pixel pair matching. By constructing feature enhancement sub-nets for different scales and superimposing the feature maps together, our technique preserves and transmits more spatial information to the backbone network, hence alleviating the common problem of detail loss in deepened network. Moreover, for each pixel, our technique employs a novel loss term to make the network learn from the classification results of similar neighbouring pixels in order to smooth out small local errors. Experiments on a new water-body dataset, namely DT-1, demonstrate that the proposed methods have improved by at least 1.24% in segmentation precision in comparison with state-of-the-art methods, including the fully connected network (FCN8S), U-Net, SegNet, RefineNet, and DeepLabv3+, which effectively captures the details of water and reduces pixel classification error of the water boundary.