![]() ![]() ![]() Take CRF as an example: in addition to learning the coefficients utilizing Structured Support Vector Machine (SSVM) or other classifiers, the testing stage of CRF also requires an inference algorithm to find the optimal label sequence by pursuing the Maximum A Posteriori (MAP).Īt present, there are two main types of graph convolution methods, namely spatial approaches and non-spectral approaches. Probabilistic graphical models can capture the appearance and spatial consistency, but they usually have a heavy computational burden. As the over-segmented image becomes graph-structure data, most of the previous superpixel-wise segmentation methods employ the probabilistic graphical models (e.g., Conditional Random Fields (CRF) ) to segment the nodes of the graphs. Superpixels can greatly improve the efficiency of the segmentation algorithm in big data while preserving the images’ edge information. The superpixel generation models usually employ the unsupervised clustering algorithms to cluster the adjacent and similar pixels together to form a superpixel. With the appearance of superpixel generation models (e.g., Simple Linear Iterative Clustering (SLIC) ), the superpixel-wise segmentation algorithms have received considerable attention. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. ![]() GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |