Ship detection from remote sensing images can provide important information for maritime reconnaissance and surveillance and is also a challenging task. Although previous detection methods including some advanced ones based on deep convolutional neural network expertize in detecting horizontal or nearly horizontal targets, they cannot give satisfying detection results for arbitrary-oriented ship detection. In this letter, we introduce a novel ship detection system that can detect arbitrary-oriented ships. In this method, a rotated region proposal networks (R ² PN) is proposed to generate multiorientated proposals with ship orientation angle information. In R ² PN, the orientation angles of bounding boxes are also regressed to make the inclined ship region proposals generated more accurately. For ship discrimination, a rotated region of interest pooling layer is adopted in the following classification subnetwork to extract discriminative features from such inclined candidate regions. The proposed whole ship detection system can be trained end to end. Experimental results conducted on our rotated ship data set and HRSD2016 benchmark demonstrate that our proposed method outperforms state-of-the-art approaches for the arbitrary-oriented ship detection task.

The detection and recognition of ships holds crucial significance across various domains, including transportation, fishing, smuggling prevention, and rescue operations. To achieve accurate and efficient ship detection and recognition, remote sensing technologies, specifically, optical and synthetic aperture radar (SAR) sensors, have been widely employed. However, each sensor modality presents its own limitations, such as cloud cover in optical images and interference in SAR images, resulting in restricted data availability and false alarms during detection. This article provides a comprehensive summary of recent advancements in the fusion of optical and SAR images for ship detection and recognition. Meanwhile, the fusion of remote sensing images and in situ data, such as automatic identification system (AIS) data, is briefly summarized, considering significant benefits and technical trends. This article highlights the immense potential of integrating these two sensor modalities to overcome individual challenges and enhance overall system performance. One of the primary challenges addressed in this work pertains to accurately matching ship targets between optical and SAR images when ships change positions. However, recently developed multimodal fusion networks and transfer learning techniques have the potential to tackle the challenges, even with unmatched optical and SAR image pairs. In addition to summarizing existing techniques, the article delves into future perspectives on fusion-based ship detection and recognition. In conclusion, this survey highlights the current state of optical and SAR image fusion techniques, pinpoints existing challenges, and outlines future research prospects, emphasizing the significance of fusion-based approaches in shaping the future of maritime applications.

Automatic building extraction from aerial or satellite images is a dense pixel prediction task for many applications. It demands a large number of clean label data to train a deep neural network for building extraction. But it is labor expensive to collect such pixel-wise annotated data manually. Fortunately, the building footprint data of geographic information system (GIS) maps provide a cheap way of generating building label data, but these labels are imperfect due to misalignment between the GIS maps and images. In this letter, we consider the task of learning a deep neural network to label images pixel-wise from such noisy label data for building extraction. To this end, we propose a general label noise-adaptive (NA) neural network framework consisting of a base network followed by an additional probability transition modular (PTM) which is introduced to capture the relationship between the true label and the noisy label. The parameters of the PTM can be estimated as part of the training process of the whole network by the off-the-shelf backpropagation algorithm. We conduct experiments on real-world data set to demonstrate that our proposed PTM can better handle noisy labels and improve the performance of convolutional neural networks (CNNs) trained on the noisy label data generated by GIS maps for building extraction. The experimental results indicate that being armed with our proposed PTM for fully CNN, it provides a promising solution to reduce manual annotation effort for the labor-expensive object extraction tasks from remote sensing images.

Aircraft detection in synthetic aperture radar (SAR) images is one challenging task due to the discreteness of aircraft scattering, the diversity of aircraft size, and the interference of background. In order to deal with these problems, a novel method named scattering enhancement and feature fusion network (SEFFNet) is here proposed to detect aircraft via combining traditional image processing and deep learning together. At first, a scattering information extraction and enhancement module (SIEEM) is proposed to highlight the scattering points of aircraft targets. Then, to more effectively focus on the location of aircraft targets, a space-to-depth coordinate attention module (SDCAM) is further designed, following which an efficient multi-scale feature fusion pyramid (FFP) is also introduced to fuse the semantic information of different layers. At last, a contextual fusion head (CFH) is built to improve the receptive field for better detecting aircraft. The experiments carried out on the popular datasets SADD and SAR-AIRcraft-1.0 show that SEFFNet is more appropriate for aircraft detection, especially the small-size aircraft detection, in comparison with other state-of-the-art (SOTA) methods. Taking the dataset SADD for example, on average, the precision, recall, F1-score, and APs values are respectively 2.8%, 2.6%, 2.7%, and 2.0% higher than the baseline network YOLOv5.

In the face of synthetic aperture radar (SAR) image object detection with different distributions of training and test data, traditional supervised learning methods cannot achieve good detection performance. Domain adaptation (DA) method has been shown to have the ability to solve this problem, but existing DA object detection algorithms all use adversarial DA theory for the detection task, which is ineffective in solving object regression localization in the detection task. In this article, to better solve the above problem, an automatic SAR image ship detection method based on feature decomposition crossing different satellites is proposed. The feature extraction layer of backbone network is divided into low level and high level, where domain-invariant feature (DIF) extractors are designed for the local features extracted from the low level and the global features extracted from the high level, respectively. We argue that the local and global features extracted from source domain and target domain contain domain-specific features (DSF) for adversarial DA and DIFs that contribute to object regression localization. Then, we decompose the local features and global features into DSF and DIF via vector decomposition method. For DSF counterpart, we introduce adversarial DA attention for feature alignment. DIF from the local features are fused into the backbone network for high-level global feature extraction. Finally, using region proposal network and adversarial domain classifier, we can get the accurate bounding box and object class of SAR image objects. Extensive experiments prove that the proposed method outperforms the state-of-the-art methods in terms of detection performance.

Ship detection using polarimetric SAR (PolSAR) images has recently been an active topic in the Earth observation field. There, how to detect small ships is an open and challenging issue. Within this context, we put forward a two-stage ship detection model, by which a novel ship detection method is proposed as well. Briefly, in the first stage, a suppression manipulation is adopted to suppress sea clutter, where the feature SVVS _O is built on the intensity information with the orientation angle compensation (OAC). In the second stage, an enhancement manipulation is further executed to highlight ships from the suppressed sea clutter, where the features PID (polarimetric intensity difference) and NsD (nonsurface degree) are first constructed with SVVS _O and a series of theoretical derivations. Then, via fusing PID and NsD together, the two-stage-based method FPAN is proposed to detect ships. To demonstrate its performance, we apply FPAN to four different L-Band PolSAR datasets. Experimental results reveal that, compared to other state-of-the-art methods, especially the DBSP _CP method, FPAN is more effective in detecting small ships. On average, its figure-of-merit (FoM) and target-to-clutter ratio (TCR) values are, respectively 9.40% and 25.18% greater than those of DBSP _CP , while the time consumption is just 58.67% of the latter.