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The performance of object instance segmentation in remote sensing images hasbeen greatly improved through the introduction of many landmark frameworksbased on convolutional neural network. However, the object densely issue stillaffects the accuracy of such segmentation frameworks. Objects of the same classare easily confused, which is most likely due to the close docking betweenobjects. We think context information is critical to address this issue. So, wepropose a novel framework called SLCMASK-Net, in which a sequence local contextmodule (SLC) is introduced to avoid confusion between objects of the sameclass. The SLC module applies a sequence of dilation convolution blocks toprogressively learn multi-scale context information in the mask branch.Besides, we try to add SLC module to different locations in our framework andexperiment with the effect of different parameter settings. Comparativeexperiments are conducted on remote sensing images acquired by QuickBird with aresolution of $0.5m-1m$ and the results show that the proposed method achievesstate-of-the-art performance.

Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module