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- 摘要
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- 实验方案
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[ACM Press the 2nd International Conference - Chengdu, China (2018.06.16-2018.06.18)] Proceedings of the 2nd International Conference on Advances in Image Processing - ICAIP '18 - MIFNet
摘要： Sea-land segmentation is of great significance to coastline extraction and ship detection. Due to the complicated texture and intensity distribution of high resolution remote sensing images, traditional methods based on threshold and artificial features are difficult to perform well. This paper presents a new multi-information fusion network (MIFNet) based on convolutional neural network. MIFNet not only considers multi-scale edges and multi-scale segmentation information, but also introduces global context information, and fuses different scales and types of information through network learning. Experiments on a set of natural-colored images from Google Earth show that our model achieves better performance than the state-of-the-art methods.
关键词： semantic segmentation,Sea-land segmentation,global context,multi-information
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Detection Without Sea-Land Segmentation for Large-Scale High-Resolution Optical Satellite Images
摘要： Ship detection is an important and challenging topic in remote sensing applications. In current literatures, sea-land segmentation is generally requested before ship detection. This makes the implementation of the methods highly complicated. Therefore, based on Faster R-CNN, this paper proposes a ship detection method for large-scale images, which does not need sea-land segmentation as pre-processing step and can detect ships directly from complicated background including sea and land. We use large-scale images consisting of GF-1 and GF-2 satellite images to test our network. Experimental results prove that the proposed method plays a role in removing the interference of objects on land.
关键词： Ship detection,deep learning,sea–land segmentation,high-resolution satellite images
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Shipnet for Semantic Segmentation on VHR Maritime Imagery
摘要： For VHR maritime images, sematic segmentation is a new research hotspot and plays an important role in coast line navigation, resource management and territory protection. Without enough labeled training data, it is a challenge to separate small objects on a large scale while segment the big area clearly. To deal with it, we propose a novel ShipNet and design a weighted loss function for simultaneous sea-land segmentation and ship detection. To prove the proposed method, we also built and opened a new dataset to the community which contains VHR multiscale maritime images. Compared with the FCN and ResNet, the proposed method got much better F1 scores 85.90% for ship class and 97.54% overall accuracy. Compared with multiscale FCN, the ShipNet could obtain details results like sharp edges. Even for images with bad quality, the ShipNet could also keep robust and get good results.
关键词： CNN,ship detection,Sea-land segmentation,remote sensing image
更新于2025-09-19 17:15:36