|Place of Conferral||北京|
|Keyword||高分辨率遥感图像 海陆分割 舰船检测 图割 深度卷积神经网络|
Sea--land segmentation and ship detection are two prevalent research domains for high-resolution remote sensing images, and have many applications in coastline extraction, sea traffic management and military monitoring, etc. During last several years, the research on sea--land segmentation and ship detection has experienced great advance. However, with the improvement of the spatial resolution of remote sensing images, there are still some factors make those two tasks challenging. First, due to the complicated intensity and texture distribution of land region, traditional methods often fail to obtain smooth segmentation results. Second, the disturbance of cloud, wave and shadow further adds difficulty for accurate sea--land segmentation and ship detection. Third, the segmentation of some thin and elongated structures around sea-land boundaries is challenging. Finally, more discriminative ship features are needed.
According to the existing problems in traditional sea--land segmentation and ship detection methods, this paper applies the prevalent segmentation and deep learning methods to high-resolution remote sensing images, and proposes some solutions. The main contributions are as follows:
1. A sea--land segmentation method based on automatic seeds selection and edge directed graph cut (GC) is proposed. The problem of user-specific strokes needed in GC is addressed by providing two automatic seeds selection schemes, namely the seeds learning method and hierarchical region merging method. For seeds learning method, the superpixel is used to replace pixel to extract training and testing samples, which can better employ local information and reduce information redundancy. Then a multi-feature descriptor fuses spectral, texture and spatial information is proposed to encode each sample. The probabilistic support vector machine (SVM) is employed to train the model, and testing samples whose probability outputs are higher than a threshold are selected as seeds. For hierarchical region merging method, the superpixel clustering and graph-based merging method are combined to extract the maximum area of sea region (MASR), which is robust to obtain smoothing results. To reduce the under-segmentation phenomenon in GC, edge prior is integrated in GC model to improve results of some thin and elongated structures around sea--land boundaries. Experiments on sea--land images demonstrate the effectiveness of proposed seeds selection and segmentation methods.
2. A structured edge network for sea--land segmentation is proposed. The convolutional neural network is applied to sea--land segmentation and two innovations are made on top of the traditional structure. First, a Laplacian smooth regularization is proposed to achieve better spatially consistent result. The Laplacian matrix is built with spectral distance of neighboring pixels. Second, a structured edge optimization method is proposed to improve the inner-class regularization of the segmentation network and boundary accuracy of the results by employing both label and structure information of edges. Experiments validate the effectiveness of proposed method by comparing with both traditional sea--land segmentation methods and convolutional neural networks.
3. An edge aware deep convolutional network for sea--land segmentation and ship detection is proposed, which unifies those two tasks into a single framework and applies the semantic segmentation network to parse the harbor image into three typical objects, e.g., sea, land and ship. First, a multi-task model is designed by simultaneously training the segmentation network and edge detection network. Hierarchical semantic features from the segmentation network are extracted to learn the edge network. Second, the outputs of edge pipeline are further employed to refine the entire model by adding an edge aware regularization, which helps to yield very desirable results that are spatially consistent and well boundary located. It also benefits the segmentation of docked ships that are quite challenging for many previous approaches. The proposed method achieves better performance on both harbor image dataset and sea--land image dataset compared with traditional methods.
|First Author Affilication||Institute of Automation, Chinese Academy of Sciences|
|程栋材. 高分辨率遥感图像海陆分割与舰船检测方法研究[D]. 北京. 中国科学院研究生院,2017.|
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