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.
翻译:遥感图像中天体分解的性能通过采用许多以进化神经网络为基础的里程碑式框架而大大改进了遥感图像中的天体例分化性能。然而,物体密度问题仍然影响这种分解框架的准确性。同一类物体很容易被混淆,这很可能是因为物体之间的对接。我们认为,背景信息对于解决这一问题至关重要。因此,我们提议了一个叫SLCMASK-Net的新颖框架,在这个框架中引入一个序列本地背景模块(SLC),以避免同一类物体之间的混淆。 SLC模块应用一系列变相区块来逐步学习掩码分支的多尺度背景信息。此外,我们试图将SLC模块添加到我们框架中的不同位置,并尝试不同参数设置的效果。对QuickBird以0.5m-1m美元分辨率获得的遥感图像进行了比较实验,结果显示,拟议的方法达到了最新性能。