In recent years, ship detection in satellite remote sensing images has become an important research topic. Most existing methods detect ships by using a rectangular bounding box but do not perform segmentation down to the pixel level. This paper proposes a ship detection and segmentation method based on an improved Mask R-CNN model. Our proposed method can accurately detect and segment ships at the pixel level. By adding a bottom-up structure to the FPN structure of Mask R-CNN, the path between the lower layers and the topmost layer is shortened, allowing the lower layer features to be more effectively utilized at the top layer. In the bottom-up structure, we use channel-wise attention to assign weights in each channel and use the spatial attention mechanism to assign a corresponding weight at each pixel in the feature maps. This allows the feature maps to respond better to the target's features. Using our method, the detection and segmentation mAPs increased from 70.6% and 62.0% to 76.1% and 65.8%, respectively.

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In recent years, with the development of remote sensing technology and the enhancement of the value of remote sensing images in military and civil fields, remote sensing image object segmentation has also received more and more attention. This paper mainly studies the application of instance segmentation based on deep convolutional neural network in the remote sensing image. This paper proposes an attention balanced feature pyramid module, which strengthens multi-level features and uses the attention module to suppress the interference features of noise in the complex background. In addiction, Soft-NMS is introduced to improve the performance of the network, and GIoU loss is introduced to improve the effect of object detection. The proposed network improves the average detection and segmentation accuracy (mAP) values from [Formula: see text] and [Formula: see text] to [Formula: see text] and [Formula: see text], respectively.