The advancement of convolutional neural networks (CNNs) has greatly promoted the development of ship detection in SAR images. The oriented ship detection is practical and imperative, but the development of its fully-supervised methods is limited by the insufficiency of data annotated in oriented bounding box (OBB). Therefore, we proposed a weakly supervised learning (WSL) paradigm to train network by only data annotated in horizontal bounding box (HBB) for oriented ship detection. The paradigm follows a coarse-to-fine prediction route, and the ships coarse orientations and high-quality pseudo-labels both mined from images are used to train the network combined with the HBB annotations. The designed orientation initialization decoder (OID) utilizes the output from the prediction branch of ship coarse orientation (PBSCO) and the regression branch to decode coarse OBBs, based on the horizontal proposals, and then these OBBs are refined by the fine prediction head, which is jointly trained by high-quality pseudo-labels and HBB annotation. Extensive experiments were conducted on the SAR ship detection dataset (SSDD) and the high resolution SAR images dataset (HRSID) with both OBB and HBB annotations, and the results show that our proposed WSL paradigm can train network to attain theAP50andRin the same level of mainstream fully-supervised methods in remote sensing.

With the rapid development of deep learning, the convolutional neural networks (CNNs) have been widely used in hyperspectral image classification (HSIC) and achieved excellent performance. However, CNNs reuse the same kernel weights over different locations, resulting in the insufficient capability of capturing diversity spatial interactions. Moreover, CNNs usually require a large amount of training samples to optimize the learnable parameters. When training samples are limited, the classification performance of CNN tends to drop off a cliff. To tackle the aforementioned issues, a novel residual spatial attention kernel generation network (RSAKGN) is proposed for HSIC. First, a spatial attention kernel generation module (SAKGM) is built to extract discriminative semantic features, which can dynamically calculate the attention weights to generate specific spatial attention kernels over different locations. Then, we combine the SAKGM with residual learning framework by embedding the SAKGM into a bottleneck residual block to obtain the residual spatial attention block (RSAB). The RSAKGN is constructed by stacking several RSABs. Experimental results on three public HSI datasets demonstrate that the proposed RSAKGN method outperforms several state-of-the-arts with small sample size.

Numerous synthetic aperture radar-automatic target recognition (SAR-ATR) methods require a large amount of training data. However, collecting SAR data is both expensive and complicated in practical applications. Recognition with the limited training data has become a vital issue in SAR-ATR. To solve this problem, we propose a recognition model combining probabilistic inference with meta-learning to transfer prior knowledge from simulated to real SAR data. First, we use various recognition tasks drawn from the simulated data to learn the global parameters of the model. Second, we draw new tasks from the real data and use the amortized inference to model a posterior distribution over task-specific parameters. Finally, we produce a predictive distribution indicating the confidence of the target classes. The experimental results demonstrate the superiority of the model in recognition tasks with a small amount of training data. We also show that introducing probabilistic inference can improve the prediction accuracy and prediction uncertainty of the model.

A graph-based approach to finding near-optimal output phase assignments for PLA minimization is presented. A distinctive feature of the approach is that it exploits the necessary and sufficient conditions to reduce the number of product terms needed for PLA implementation and permits the use of existing graph algorithms to solve the PLA output phase optimization problem. The work is based on the transformation of a PLA into a graph whose vertices are the set of conditions required to reduce each product term of the PLA, and whose edges represent the relations between these conditions. Cliques (completely connected subgraphs) in the graph correspond to the output phase assignments required to reduce the product terms represented by the vertices in the cliques. The optimal PLA output phase assignment problem is then formulated as the well-studied problem in graph theory: finding maximum cliques in graphs. Using an existing algorithm for locating cliques in graphs, a modified greedy algorithm is proposed to compute output phase assignments for logic functions in polynomial time. Experimental results using a number of benchmark functions show that the graph-based approach can achieve optimal or near-optimal output phase assignment for PLA minimization and can lead to PLA's with fewer product terms than achieved using existing approaches.>