SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression

The ship detection task using optical remote sensing images is important for in maritime safety, port management and ship rescue. With the wide application of deep learning to remote sensing, a series of target detection algorithms, such as faster regions with convolution neural network feature (R-C...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-01, Vol.13 (3), p.499
Main Authors: Wang, Zhenqing, Zhou, Yi, Wang, Futao, Wang, Shixin, Xu, Zhiyu
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Artificial neural networks
Coders
Convolution
Deep learning
Feature maps
Gaussian heatmap
Machine learning
Neural networks
optical remote sensing images
Post-production processing
Receptive field
Regression
Remote sensing
Safety management
SDGH-Net
Semantics
Sensors
ship detection
Ships
Target detection
Training
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Summary:The ship detection task using optical remote sensing images is important for in maritime safety, port management and ship rescue. With the wide application of deep learning to remote sensing, a series of target detection algorithms, such as faster regions with convolution neural network feature (R-CNN) and You Only Look Once (YOLO), have been developed to detect ships in remote sensing images. These detection algorithms use fully connected layer direct regression to obtain coordinate points. Although training and forward speed are fast, they lack spatial generalization ability. To avoid the over-fitting problem that may arise from the fully connected layer, we propose a fully convolutional neural network, SDGH-Net, based on Gaussian heatmap regression. SDGH-Net uses an encoder–decoder structure to obtain the ship area feature map by direct regression. After simple post-processing, the ship polygon annotation can be obtained without non-maximum suppression (NMS) processing. To speed up model training, we added a batch normalization (BN) processing layer. To increase the receptive field while controlling the number of learning parameters, we introduced dilated convolution and added it at different rates to fuse the features of different scales. We tested the performance of our proposed method using a public ship dataset HRSC2016. The experimental results show that this method improves the recall rate of ships, and the F-measure is 85.05%, which surpasses all other methods we used for comparison.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13030499