Detecting aircraft in complex remote sensing scene has significant value for military and civilian applications. To overcome the interference of complex environmental factors and achieve high-accuracy detection performance, the comprehensive utilization of optical and SAR images for object detection has become a promising research direction. However, currently there are problems with optical and SAR fusion detection, such as difficulty in obtaining paired registration training data and incomplete consideration of feature elements in fusion model. To tackle these challenges, we present an aircraft detection method based on optical-SAR complementarity-aware feature fusion. Firstly, an unpaired image translation model based on scattering feature enhancement GAN (SFEG) is designed to generate SAR images that are pixel-level registered with the input optical image. On this basis, a complementarity-aware feature fusion detection network (CFFDNet) combining differential feature spatial-aware complementary (DFSC) units and gate-generated weighted fusion (GWF) units is proposed to enhance the effective features of single source image while improving the complementary fusion effect of multimodal features. Experiments on CORS-ADD and MAR20 datasets demonstrate that our method outperforms the compared classical single-modal and multimodal detection models. The latest code is available soon at: https://github.com/JimmyRSlab/Complementarity-aware-Feature-Fusion-for-Aircraft-Detection-via-Unpaired-Opt2SAR-Image-Translation.

Ship recognition in remote sensing imagery is crucial for numerous applications such as monitoring maritime security, preventing illegal activities and implementing environmental protection. However, most existing research rarely focuses on both the complexity of the scene and the fine-grained recognition of targets, which limits the accuracy and applicability of ship recognition technology. To propel advancements in ship recognition methodology, we propose a new dataset named Fine-Grained Ship Recognition in Complex Scenarios (FGSRCS), which contains 17 typical categories of large and medium-sized ships from 280 widely distributed ports. For the richness of the image characteristics, the dataset images are collected from multiple satellite platforms, such as Ikonos, Jilin-1, OrbView, Pleiades and WorldView, and the time span of the data covers the past 20 years. To ensure the diversity of scenarios, the dataset mainly considers five complex scenarios, including thick clouds, mists, shadows, sea clutter and ground facilities, which helps to train and improve the algorithm applicability to practical application scenarios. Furthermore, we conduct experiments with ten state-of-the-art recognition algorithms on FGSRCS dataset, providing a benchmark for algorithm application. The research result can furnish both theoretical insights and practical guidance for the development of future ship recognition models. The dataset is available on https://github.com/dwddw/FGSRCS.

In the field of remote sensing, detection in dimly lit or shadowed areas has traditionally been difficult because of detector noise. Given that noise in real-world images of remote sensing exhibits spatial correlation, existing self-supervised methods encounter difficulties in reconciling the suppression of spatially correlated noise with the preservation of local texture details. To address this challenge, we propose a self-supervised model that combines blind-spot feature extraction with diffusion-based texture generation to fine denoising of real-world images under adverse conditions. We first introduce a blind-spot feature extraction structure based on the fusion of U-Net with blind-spot net (UBSN) and blind transformer (BTF). In UBSN, we integrate multistride blind-spot convolution (BSC) + dilated convolution (DC) feature extraction nodes and employ a Reshuffle strategy in skip-layer connections to maintain large-scale blind-spot characteristics. Additionally, we design a transformer structure for blind spot between patches to remove the noise with spatial correlations while ensuring global feature acquisition. Subsequently, to restore texture details blurred by the blind-spot structures, we introduce a texture generation diffusion structure during model training, achieving a balance between large-scale blind-spot characteristics and local rich texture details. Experimental results demonstrate that our approach outperforms other self-supervised denoising methods, even some methods leveraging unpaired images, without the need for parameters related to on-orbit satellite detectors.

Ship detection plays a pivotal role in numerous military and civil applications, yet detecting ships in complex maritime and aerial environments remains a challenging task. While several publicly available datasets for ship detection have been introduced by researchers, most of them do not adequately address the impacts of diverse and intricate environmental factors, which makes the trained algorithms difficult to apply for practical application scenes involving clouds, sea clutter, complex lighting, and facility interferences, limiting the effectiveness and robustness of the detection models. To advance the field of ship detection method research, we propose a high-quality dataset named ship collection in complex optical scene (SCCOS), which is obtained from multiple platform sources including Google Earth, Microsoft map, Worldview-3, Pleiades, Orbview-3, Jilin-1, and Ikonos satellites. The dataset comprehensively considers complex scenes such as thin clouds, mist, thick clouds, light shadows, sea clutter, and port facilities. Additionally, we conduct experiments on this dataset with 11 representative detection algorithms and establish a performance benchmark, which can provide the theoretical basis and practical reference for the design and optimization of subsequent ship detection models. The latest dataset is available at:https://github.com/JimmyRSlab/Dataset-and-Benchmark-for-Ship-Detection-in-Complex-Optical-Remote-Sensing-Image.

Infrared small target detection is widely used in remote sensing fields. However, the application scenes of space-based remote sensing imaging often lead to problems such as small target scale, weak energy, and serious influence by strong clutters. At present, traditional methods are often difficult to adapt to the change of target scale. And deep learning methods are often difficult to extract small target features, and the change in imaging characteristics also brings challenges to the generalization ability. To complement each other’s advantages, we propose an infrared small target detection method that combines the traditional methods with the deep learning methods. First, we construct a multi-stage feature extraction network for guiding the typical multi-scale traditional filtering results to progressively fuse. Secondly, we propose a multi-scale attention supervision module to adjust the semantic consistency of different stages, improving the network generalization ability. Next, a dynamic weight convolution module is utilized to obtain the optimal distribution of grayscale in the neighborhood. Finally, we use the background modelling results to suppress the background, effectively weakening the influence of background clutters and enhancing the target contrast. Experimental results show that the proposed method has good detection results for targets with different scales and signal-to-clutter ratios in a variety of complex scenes. Compared with the typical methods, our method has better detection performance and generalization ability.

Small target detection in remote sensing images has considerable significance in practical applications such as military dynamic discrimination and traffic monitoring. However, the limited appearance features of small-scale targets and the widespread false alarm sources make small target detection in remote sensing images a tough challenge. To address these problems, we propose a novel small detection method by employing an adaptive multi-level feature fusion module (AMFFM) and an attention-augmented high-resolution head (AAHRH). Specifically, AMFFM is designed to suppress the interference of false alarm sources in complicated scenes. We upsample the high-level features by the context modeling of semantic information and refine the low-level features for noise removal. Then the enhanced multi-level features are fused based on the spatial and channel significance. After that, AAHRH is put forward to enhance the perception of small targets by embedding cross-dimension interaction with the attention mechanism. The prediction heads are reconstructed with high-resolution layers to improve the detection performance in densely distributed scenes. We conduct dilated and comparison experiments on a constructed small car dataset, a public small ship dataset, and the VEDAI dataset. The experimental results on two datasets verify the effectiveness and robustness of the proposed method with the state-of-the-art performance.

Aircraft detection in remote sensing images is significant in both military and civilian fields, such as air traffic control and battlefield dynamic monitoring. Deep learning methods can achieve promising detection performance with sufficient and labeled samples. However, current aircraft datasets are mainly from a single data source and lack diverse scenes and targets, making it difficult to train a robust and generalized detector. Therefore, we manually label and construct a complex optical remote sensing aircraft target detection dataset (CORS-ADD) from Google Earth and multiple satellites such as WorldView-2, WorldView-3, Pleiades, Jilin-1, and IKONOS. It contains 7,337 images covering typical airports and various rare scenes, including the aircraft carrier, ocean and land with flying aircraft. The dataset consists of 32,285 civil and military aircraft instances, including bombers, fighters, and early warning aircraft. These targets range from 4×4 pixels to 240×240 pixels and are all labeled with both horizontal bounding box (HBB) and oriented bounding box (OBB) annotations. The various scenes and sufficient instances can fully support the training and evaluation of data-driven algorithms. Meanwhile, based on the constructed dataset, we train and evaluate several detectors to provide a benchmark and help promote the development of aircraft detection techniques.

With the continuous increase in the resolution of optical remote sensing satellites, the influence of space variations on the image quality cannot be ignored, especially in new imaging systems such as thin-film diffraction and rectangular rotating pupils. This paper was conducted to analyze the influence of space variability on restoration performances of different methods, then a new processing strategy of space-variant images is proposed. According to the analytical experiment results, we suggest using the block method when the PSV < 0.20% and otherwise selecting the global method. In order to ensure the final image quality, we also suggest controlling the PSV within 0.28% when designing optical systems. This study can provide a foundation for optimizing the design of front-end optical systems and selecting back-end processing methods in engineering applications.

Non-functional properties reflect the quality of a software system and are essential for a successful software system, but analysis of non-functional properties is less well studied compared to that of functional properties. Performance, dependability and performability are most concerned non-functional properties in lifecritical systems. In this paper, a methodology is proposed to analyze dependability and performability using a modeling and analyzing framework called SAM. By incorporating stochastic information into a SAM model, dependability and performability as well as functional properties can be analyzed at software architecture level using proper analysis techniques under the uniform SAM framework.