Ship detection has been playing a significant role in the field of remote sensing for a long time, but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection, and the redundancy of the detection region. In order to solve these problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ships in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving problems resulting from the narrow width of the ship. Compared with previous multiscale detectors such as Feature Pyramid Network (FPN), DFPN builds high-level semantic feature-maps for all scales by means of dense connections, through which feature propagation is enhanced and feature reuse is encouraged. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multiscale region of interest (ROI) Align for the purpose of maintaining the completeness of the semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has state-of-the-art performance.

Boundary information is essential for the semantic segmentation of remote sensing images. However, most existing methods were designed to establish strong contextual information while losing detailed information, making it challenging to extract and recover boundaries accurately. In this paper, a boundary-sensitive network (BSNet) is proposed to address this problem via dynamic hybrid gradient convolution (DHGC) and coordinate sensitive attention (CSA). Specifically, in the feature extraction stage, we propose dynamic hybrid gradient convolution (DHGC) to replace vanilla convolution, which adaptively aggregates one vanilla convolution kernel and two gradient convolution kernels (GCKs) into a new operator to enhance boundary information extraction. The GCKs are proposed to explicitly encode boundary information, which are inspired by traditional Sobel operators. In the feature recovery stage, the coordinate sensitive attention (CSA) is introduced. This module is used to reconstruct the sharp and detailed segmentation results by adaptively modeling the boundary information and long-range dependencies in the low-level features as the assistance of high-level features. Note that DHGC and CSA are plug-and-play modules. We evaluate the proposed BSNet on three public data sets: the ISPRS 2-D semantic labeling Vaihingen, Potsdam benchmark and iSAID data set. The experimental results indicate that BSNet is a highly effective architecture that produces sharper predictions around object boundaries and significantly improves the segmentation accuracy. Our method demonstrates superior performance on the Vaihingen, Potsdam benchmark and iSAID data set, in terms of the mean F1, with improvements of 4.6%, 2.3% and 2.4% over strong baselines, respectively. The code and models will be made publicly available.

Multi-class object detection in remote sensing imagery is an important and challenging topic in computer vision. Compared with the object detection of natural scenes, remote sensing object detection has some challenges such as scale diversity, arbitrary directions and densely packed objects. To resolve these problems, this paper presents a scale-aware rotated object detection. Firstly, we propose a novel feature fusion module, which takes full advantage of high-level semantic information and low-level high resolution feature. The new feature maps are more suitable for detecting objects with a large difference in scale. Meanwhile, we design a specific weighted loss, which contains an intersection-over-union (IoU) loss and a smooth L1 loss to further address the scale diversity. Besides, in order to detect oriented and densely packed objects more accurately, we propose a normalization strategy for the representation of rotating bounding box. Our method is evaluated on two public aerial datasets DOTA and HRSC2016, and achieves competitive performances. On DOTA, we boost the mean Average Precision (mAP) to 72.95% on oriented object detection.

With the growth of remote sensing images, un-derstanding image content automatically has attracted many researchers' interests in deep learning for remote sensing image. Inspired from the natural image captioning, the model with CNN-RNN as the backbone and supplemented by attention has been widely used in remote sensing image captioning. However, it is inefficient for the current attention layer to simultaneously mine hidden foreground from the background of remote sensing image and perform feature interactive learning. Meanwhile, the new mainstream language model has recently surpassed the traditional LSTM in sentence generation. For solving the above problems, in this paper, we proposed a novel thought to make the flat remote sensing images stereoscopic by separating the fore- and background. Based on hierarchical image informa-tion, we designed a novel Deformable Transformer equipped with deformable scaled dot-product attention to learn multi-scale feature from fore- and background through the powerful interactive learning ability. Evaluations are conducted on Four classic remote sensing image captioning datasets. Compared with the state-of-the-art methods, our Transformer variant achieves higher captioning accuracy.

As a challenging sub-task of event argument extraction, implicit event argument extraction seeks to identify document-level arguments that play direct or implicit roles in a given event. Prior work mainly focuses on capturing direct relations between arguments and the event trigger; however, the lack of reasoning ability imposes limitations to the extraction of implicit arguments. In this work, we propose an A rgument-argument R elation-enhanced E vent A rgument extraction (AREA) learning framework to tackle this issue through reasoning in event frame-level scope. The proposed method leverages related arguments of the expected one as clues, and utilizes such argument-argument dependencies to guide the reasoning process. To bridge the distribution gap between oracle knowledge used in the training phase and the imperfect related arguments in the test stage, we introduce a conventional knowledge distillation strategy to drive a final model that can work without extra inputs by mimicking the behaviour of a well-informed teacher model. In addition, considering that conventional knowledge distillation methods transfer knowledge individually, we integrate it with a novel relational knowledge distillation mechanism to explicitly capture the structural mutual argument-argument relation. Moreover, since the training process is not compatible with the real situation, a curriculum learning method is further introduced to make the training process smoother. Experimental results demonstrate that the learning framework obtains state-of-the-art performance on the RAMS and Wikievents datasets. Ablation study and further discussion also show it could handle long-range dependency and implicit argument problems effectively.

Semantic segmentation of remote sensing images encounters four significant difficulties: 1) complex backgrounds, 2) large-scale differences, 3) numerous small objects, and 4) extreme foreground-background imbalance. However, the existing generic semantic segmentation models mainly focus on the modeling context information and rarely focus on these four issues. This article presents an enhanced remote sensing image semantic segmentation framework to solve these problems through the Hierarchical Atrous Pyramid module (HASP) and spatial-adaptive convolution based FPN decoder framework (SPA-FPN). On the one hand, HASP solved the problem of complex backgrounds and large-scale differences by further enlarging the receptive field of the network through the cascade of atrous convolution with various rates. On the other hand, spatial adaptive convolution is embedded in FPN decoder framework step by step to solve the problems of numerous small objects and extreme foreground-background imbalance. Besides, a boundary-based loss function is constructed to help the network optimize the relevant segmentation results. Extensive experiments over iSAID and ISPRS Vaihingen datasets reflect the superiority of the presented structure to conventional state-of-the-art semantic segmentation approaches. The code will be accessible.