Dense Representative Points-Guided Rotated-Ship Detection in Remote Sensing Images

The presence of shadows in high-resolution (HR) remote-sensing images reduces object detection accuracy. To address this problem, in this paper, we proposed a deep neural network algorithm for shadow detection by using the AISD and SSAD remote-sensing shadow image datasets. To improve the ability to extract spatial information from feature maps, we developed a cross-spatial attention module that focuses on semantic information in the horizontal and vertical directions at each position point on the remote-sensing image. This module overcomes the limitations of existing technologies in accurately judging small areas and suspected shadow areas and in missing or incorrectly detected shadow areas. In addition, to improve the ability to extract shadow features and the accuracy of shadow detection in remote-sensing images, we developed a channel attention module that assigns more attention to channels that conform to the shadow color characteristics. The network architecture comprises an encoder – decoder structure, with ResNeXt50 used as the backbone for the encoder and a multi resolution parallel fusion (MRPF) designed for the decoder; cross-spatial and channel attention were incorporated into the decoder unit. Experimental results demonstrated the superior performance of the proposed algorithm, with an F1 score of 92.6% for the shadow category on the test set, thus, outperforming other algorithms and making the proposed method an effective solution for shadow detection in HR remote-sensing images.

Object detection plays a crucial role in scene understanding and has extensive practical applications. In the field of remote sensing object detection, both detection accuracy and robustness are of significant concern. Existing methods heavily rely on sophisticated adversarial training strategies that tend to improve robustness at the expense of accuracy. However, detection robustness is not always indicative of improved accuracy. Therefore, in this paper, we research how to enhance robustness, while still preserving high accuracy, or even improve both simultaneously, with simple vanilla adversarial training or even in the absence thereof. In pursuit of a solution, we first conduct an exploratory investigation by shifting our attention from adversarial training, referred to as adversarial fine-tuning, to adversarial pretraining. Specifically, we propose a novel pretraining paradigm, namely structured adversarial self-supervised (SASS) pretraining, to strengthen both clean accuracy and adversarial robustness for object detection in remote sensing images. At a high level, SASS pretraining aims to unify adversarial learning and self-supervised learning into pretraining and encode structured knowledge into pretrained representations for powerful transferability to downstream detection. Moreover, to fully explore the inherent robustness of vision Transformers and facilitate their pretraining efficiency, by leveraging the recent masked image modeling (MIM) as the pretext task, we further instantiate SASS pretraining into a concise end-to-end framework, named structured adversarial MIM (SA-MIM). SA-MIM consists of two pivotal components, structured adversarial attack and structured MIM (S-MIM). The former establishes structured adversaries for the context of adversarial pretraining, while the latter introduces a structured local-sampling global-masking strategy to adapt to hierarchical encoder architectures. Comprehensive experiments on three different datasets have demonstrated the significant superiority of the proposed pretraining paradigm over previous counterparts for remote sensing object detection. More importantly, regardless of with or without adversarial fine-tuning, it enables simultaneous improvements on detection accuracy and robustness as expected, promisingly alleviating the dependence on complicated adversarial fine-tuning.

For remorte sensing image object detection tasks in the small object feature, extraction ability is insufficient and difficult to locate, and other problems. This paper proposes an improved algorithm for small object detection in remote sensing images based on a window self-attention mechanism. On the basis of You Only Look Once (YOLO)v5s, a shallow feature extraction layer with four times downsampling is added to the feature fusion pyramid and the window self-attention mechanism is added to the Path Aggregation Network. Experiments show that the improved model obtained the Mean Average Precision (mAP) of 78.3% and 91.8% on the DIOR and Remote Sensing Object Detection public data sets with frames per second of 65 and 51, respectively. Compared with the basal YOLOv5s network, the mAP has improved by 5.8% and 3.3%, respectively. Compared with other object detection methods, the detection accuracy and real-time performance have been improved.

ABSTRACTIn this article, we propose a novel difference image (DI) creation method for unsupervised change detection in multi-temporal multi-spectral remote-sensing images based on deep learning theory. First, we apply deep belief network to learn local and high-level features from the local neighbour of a given pixel in an unsupervised manner. Second, a back propagation algorithm is improved to build a DI based on selected training samples, which can highlight the difference on changed regions and suppress the false changes on unchanged regions. Finally, we get the change trajectory map using simple clustering analysis. The proposed scheme is tested on three remote-sensing data sets. Qualitative and quantitative evaluations show its superior performance compared to the traditional pixel-level and texture-level-based approaches.

Object detection has a great significance to remote sensing image recognition. Conventional object detection methods by using horizontal bounding box have shown good performance in general images. However, because of looking down perspective of remote sensing images, rotation bounding box is more suitable and precise for object detection. Although rotation bounding box has been researched by some works for remote sensing images, most of them are still two stages. In this paper, by improvement of YOLO architecture, we propose an one-stage object detection method to predict rotation bounding box for remote sensing images. Firstly, in the data preprocessing stage, an image cropping operation is utilized instead of zooming operation to generate suitable input data for network. Meanwhile, in order to avoid an object from being divided, an overlapping band is set during cropping. Secondly, a novel rotation bounding box representation method is introduced, and a corresponding loss function is designed in training process. Experiments on the Dota dataset demonstrate that our method outperforms state-of-the-art rotation objects detection methods, in terms of mAP, our method achieves 73.50%mAP, which is higher than methods based on rotation bounding box by 2.0%.

With the wide applications of remote sensing technology in engineering, the demands of efficient object detection algorithms for remote sensing images have also been significantly increased in recent years. Traditional detection methods have the shortcomings of low accuracy and poor robustness, which is difficult to be applied to remote sensing images with complex background and varieties of objects. Recently, the deep convolutional neural networks have already shown great advances in object detection and outperformed many traditional methods. In this work, we study the performance of a region proposal-based method, Mask R-CNN, for detecting airplane and ship in remote sensing images. Specifically, we add an FPN module to improve the accuracy of small objects detection, and a mask branch is used to describe the shape of objects. In addition, we used a series of data augmentation strategies during training for meeting the CNN’s requirement of training samples diversity. The experimental results show that our model has superior performance in object detection of remote sensing images.

Illegal waste dumping not only encroaches on land resources but also threatens the health of the surrounding residents. The traditional artificial waste monitoring solution requires professional workers to conduct field investigations. This solution not only requires high labor resources and economic costs but also demands a prolonged cycle for updating the monitoring status. Therefore, some scholars use deep learning to achieve automatic waste detection from satellite imagery. However, relevant models cannot effectively capture multi-scale features and enhance key information. To further bolster the monitoring efficiency of urban solid waste, we propose a novel multi-scale context fusion network for solid waste detection in remote sensing images, which can quickly collect waste distribution information in a large-scale range. Specifically, it introduces a new guidance fusion module that leverages spatial attention mechanisms alongside the use of large kernel convolutions. This module helps guide shallow features to retain useful details and adaptively adjust multi-scale spatial receptive fields. Meanwhile, it proposes a novel context awareness module based on heterogeneous convolutions and gating mechanisms. This module can effectively capture richer context information and provide anisotropic features for waste localization. In addition, it also designs an effective multi-scale interaction module based on cross-guidance and coordinate perception. This module not only enhances critical information but also fuses multi-scale semantic features. To substantiate the effectiveness of our approach, we conducted a series of comprehensive experiments on two representative urban waste detection datasets. The outcomes of relevant experiments indicate that our methodology surpasses other deep learning models. As plug-and-play components, these modules can be flexibly integrated into existing object detection frameworks, thereby delivering consistent enhancements in performance. Overall, we provide an efficient solution for monitoring illegal waste dumping, which contributes to promoting eco-friendly development.

With the continuous growth of maritime activities and the shipping trade, the application of maritime target detection in remote sensing images has become increasingly important. However, existing detection methods face numerous challenges, such as small target localization, recognition of targets with large aspect ratios, and high computational demands. In this paper, we propose an improved target detection model, named YOLOv5-ASC, to address the challenges in maritime target detection. The proposed YOLOv5-ASC integrates three core components: an Attention-based Receptive Field Enhancement Module (ARFEM), an optimized SIoU loss function, and a Deformable Convolution Module (C3DCN). These components work together to enhance the model’s performance in detecting complex maritime targets by improving its ability to capture multi-scale features, optimize the localization process, and adapt to the large aspect ratios typical of maritime objects. Experimental results show that, compared to the original YOLOv5 model, YOLOv5-ASC achieves a 4.36 percentage point increase in mAP@0.5 and a 9.87 percentage point improvement in precision, while maintaining computational complexity within a reasonable range. The proposed method not only achieves significant performance improvements on the ShipRSImageNet dataset but also demonstrates strong potential for application in complex maritime remote sensing scenarios.

Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, which is developed on the basis of RTMDet. Specifically, the AALFF method incorporates an adjacent layer feature fusion enhancement (ALFFE) module, designed to capture high-level semantic information and accurately locate object spatial positions. ALFFE also effectively preserves small objects by fusing adjacent layer features and employs involution to aggregate contextual information in a wide spatial range for object essential features extraction in complex backgrounds. Additionally, the adaptive spatial feature fusion (ASFF) module is introduced to guide the network to select and fuse the crucial features to improve the adaptability to objects with different sizes. The proposed method achieves mean average precision (mAP) values of 77.1%, 88.9%, and 95.7% on the DIOR, HRRSD, and NWPU VHR-10 datasets, respectively. Notably, our approach achieves mAP75 values of 60.8% and 79.0% on the DIOR and HRRSD datasets, respectively, surpassing the state-of-the-art performance on the DIOR dataset.

Aiming at the problem of low target detection accuracy caused by the close arrangement of targets, complex feature information, and numerous small targets in remote sensing images, this paper proposes a target detection algorithm based on the improved Yolov5 algorithm for remote sensing images. Firstly, this paper designs a remote sensing image target detection algorithm C²-YOLO based on multi-level feature fusion. Combined with the attention mechanism module to focus on the spatial location information of the target, which improves the extraction effect of the features of the target of interest. The network uses the CARAFE upsampling operator with a larger sensing field to better utilize the surrounding information. The proposal is to incorporate a predictive header hl that is generated from a low-level, high-resolution feature map to improve the recognition of tiny objects. The problem of large target scales in remote sensing images and poor detection of small targets has been improved. Angular loss is also added to the loss function to improve the detection of rotating targets. Finally, the accuracy comparison and experimental effect visualization are carried out on the dataset, and the experimental results show that the improvement work in this paper achieves better results no matter comparing the original YOLOv5 detection algorithm or other types of detection algorithms. Specifically, the accuracy of C²-YOLO on the NWPU dataset 1-shot recognition task is 78.45%, which is much higher than that of other detection algorithms, and is able to reach a superior performance in the remote sensing image target detection task.

Recently, object detection has gained significant progress in remote sensing images. Nevertheless, we conclude two defects in remote sensing image object detection. At first, most methods rely on feature pyramid, but the features of different levels would influence each other when we use top-down operation. Second, the traditional label assignment strategy cannot assign suitable labels, as it adopts the fixed intersection over union (IoU) threshold to divide positive samples and negative samples during training. According to the problems we pointed out, a simple yet effective framework is employed to eliminate these two limitations. It integrates two novel components: aware feature pyramid network (AFPN) and group assignment strategy (GAS). AFPN is to mitigate the adverse effects caused by the first problem. Specifically, it learns a vector for the higher level features in the feature pyramid to obtain clean features. As for the second limitation, we recommend a new label assignment strategy named GAS to address this problem. Samples will be grouped according to their overlaps with ground truth, and then, they are assigned to positive or negative labels in each group. Extensive experiments are conducted on the large-scale object detection dataset DIOR and DOTA. With the newly introduced two key components, our model significantly improves the detection accuracy. Without bells and whistles, our proposed method achieves 2.0% and 1.9% higher mean average precision (mAP) than Faster R-CNN with FPN when using ResNet50 and ResNet101 as the backbones, respectively. Finally, we obtain 73.3% mAP on the DIOR dataset without any tricks. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hm-better/dior_detect</uri> .

Oriented object detection in remote sensing images remains a challenging task due to arbitrary target rotations, extreme scale variations, and complex backgrounds. However, current rotated detectors still face several limitations: insufficient orientation-sensitive feature representation, feature misalignment for rotated proposals, and unstable optimization of rotation parameters. To address these issues, this paper proposes an enhanced Rotation-Sensitive Feature Pyramid Network (RSFPN) framework. Building upon the effective Oriented R-CNN paradigm, we introduce three novel core components: (1) a Dynamic Adaptive Feature Pyramid Network (DAFPN) that enables bidirectional multi-scale feature fusion through semantic-guided upsampling and structure-enhanced downsampling paths; (2) an Angle-Aware Collaborative Attention (AACA) module that incorporates orientation priors to guide feature refinement; (3) a Geometrically Consistent Multi-Task Loss (GC-MTL) that unifies the regression of rotation parameters with periodic smoothing and adaptive weight mechanisms. Comprehensive experiments on the DOTA-v1.0 and HRSC2016 benchmarks show that our RSFPN achieves superior performance. It attains a state-of-the-art mAP of 77.42% on DOTA-v1.0 and 91.85% on HRSC2016, while maintaining efficient inference at 14.5 FPS, demonstrating a favorable accuracy-efficiency trade-off. Visual analysis confirms that our method produces concentrated, rotation-aware feature responses and effectively suppresses background interference. The proposed approach provides a robust solution for detecting multi-oriented objects in high-resolution remote sensing imagery, with significant practical value for urban planning, environmental monitoring, and security applications.

Aircraft detection in remote sensing images is always the research hotspot but a challenging task for the variations of aircraft type, pose, size and complex background. The paper proposes a region-based convolutional neural network to detect aircrafts. To enhance the learning ability of the network, a multi-resolution aircraft remote sensing dataset is collected from Google Earth. Then, the detection model is trained end to end by fine-tuning on the obtained dataset and realizes automatic aircraft recognition and positioning. Experiments show that the proposed method outperforms state-of-the-art method on the same dataset and the requirement for real-time can be satisfied simultaneously.

Ship detection in remote sensing plays an important role in civil and military fields. Owing to the complex background and uncertain direction, ship detection is full of challenge by using the commonly used object-detection methods. In this article, a new framework for detecting the arbitrary direction ships is proposed based on the improvement in the Faster region-based convolutional network (R-CNN), in which the shape of the bounding box is described by three sides, namely, vertical side, horizontal side, and short side, respectively. The inclination of the ship is obtained by calculating the arc-tangent value of the vertical side to the horizontal side. First, the better performing ResNet-101 is adopted to extract features over an entire image, which are shared by the region proposal network (RPN) and the head network. Then, the multidirection proposal regions that may contain ships are generated by the RPN. Next, the global and local features of the proposal regions are combined as the whole features of the regions by a multiregion feature-fusion (MFF) module, which can provide more detailed information of the regions. Finally, the head network uses the whole features of the proposal regions for bounding-box recognition through multitask learning, including classification, regression, and incline direction prediction (left or right). The proposed method is tested and compared with other state-of-the-art ship-detection methods on two open remote-sensing data sets and some large-scale and real images. The experimental results validate that the proposed approach has achieved better performance.

Aggregating transformers and CNNs for salient object detection in optical remote sensing images