  The performance of object instance segmentation in remote sensing images has
been greatly improved through the introduction of many landmark frameworks
based on convolutional neural network. However, the object densely issue still
affects the accuracy of such segmentation frameworks. Objects of the same class
are easily confused, which is most likely due to the close docking between
objects. We think context information is critical to address this issue. So, we
propose a novel framework called SLCMASK-Net, in which a sequence local context
module (SLC) is introduced to avoid confusion between objects of the same
class. The SLC module applies a sequence of dilation convolution blocks to
progressively learn multi-scale context information in the mask branch.
Besides, we try to add SLC module to different locations in our framework and
experiment with the effect of different parameter settings. Comparative
experiments are conducted on remote sensing images acquired by QuickBird with a
resolution of $0.5m-1m$ and the results show that the proposed method achieves
state-of-the-art performance.
