Semantic segmentation of remote sensing ship image via a convolutional neural networks model

摘要

Semantic segmentation of remote sensing ship targets is one of the most challenging works in image processing, especially for small and multi-scale ship target detection. To solve these problems, an efficient method based on convolutional neural networks (CNN) to detect ship targets is proposed. This method introduces the attention model to the network to enhance the characteristics of small targets and combines atrous convolution with traditional CNN to increase the receptive field. To preserve the information lost by pooling, the proposed method uses the passthrough layer method to retain more features and concatenate the high- and low-resolution features. To verify the effectiveness of the method proposed in this study, the performance was evaluated by using precision, recall, F1-Score, mean intersection-over-union (IoU), and pixel accuracy measurements. These performances are all higher than the traditional semantic segmentation network SegNet. Mean IoU increases to 0.783 and pixel accuracy increases to 0.935. This method can conclusively identify ship targets in remote sensing images and has a certain reference value for remote sensing target detection.