Open AccessSemantic segmentation of remote sensing ship image via a convolutional neural networks model
Author(s) -
Wang Wenxiu,
Fu Yutian,
Dong Feng,
Li Feng
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5914
Subject(s) - computer science , convolutional neural network , artificial intelligence , segmentation , pooling , pixel , pattern recognition (psychology) , convolution (computer science) , image segmentation , precision and recall , remote sensing , computer vision , artificial neural network , geography
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.