No abstract

No abstract

No abstract

Multi-feature SAR ship classification aims to build models that can process, correlate, and fuse information from handcrafted features and deep features. Although handcrafted ones provide rich expert knowledge, current fusion methods do not thoroughly investigate the relatively important of handcrafted features with deep features, feature contribution imbalance, and the way features learn collaboratively. In this paper, a novel multi-feature collaborative fusion network with deep supervision (MFCFNet) is proposed to effectively realize handcrafted feature and deep feature fusion in SAR ship classification task. Specifically, our framework mainly includes two types of feature extraction branches, a knowledge supervision and collaboration module, and a feature fusion and contribution assignment module. The former module improves the feature map quality learned by each branch through auxiliary feature supervision, and introduces synergy loss to facilitate the information interaction between deep features and handcrafted features. The latter utilizes an attention mechanism to adaptively balance the importance among various features, and to assign the corresponding feature contribution to the total loss function based on the generated feature weights. We conduct extensive experimental and ablation studies on two public OpenSARShip-1.0 and FUSAR-Ship datasets, and the results show that MFCFNet is effective and outperforms single deep feature and multi-feature models based on previous Internal FC Layer and Terminal FC Layer fusion, and exhibits better performance than the current state-of-the-art methods.

Abstract. Obtaining maximal benefit is usually the most important goal pursued by Grid resource/service provider. As providers and users being noncooperative inherently, it is a fundamental challenge to design a resource allocation strategy which seems to be fair. In order to adapt to large-scale Grid environment, we adopted a hierarchical grid structure with bundle tasks to describe the Grid system. A model called Intra-Site Cooperative-game of Taskbundle (ISCT) was proposed, in which all subordinate resources participated in making profits. We calculated task market price based on the theoretical proof that the system would gain maximal global benefit if and only if it was in a balanced state. Then we determined the task allocation solution with solving the task assignment amount vector. An Intra-Site Global Benefit Maximization Allocation for Task-bundle (ISGBMAT) was presented, which converted the Grid task-bundle allocation problem into an iteration process involving retail price, market price and assignment amount of tasks. Extensive simulation experiments with real workload traces were conducted to verify our algorithm. The experimental results indicated that ISGBMAT could provide an effective solution with global benefit and completion time optimization and also adapt to dynamic Grid market.