Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remains challenging due to the distinct physical properties of these two types of data and the absence of effective fusion guidance. In this paper, a novel band prompting aided data fusion framework is proposed for LCZ classification, namely BP-LCZ, which utilizes textual prompts associated with band groups to guide the model in learning the physical attributes of different bands and semantics of various categories inherent in SAR and multi-spectral data to augment the fused feature, thus enhancing LCZ classification performance. Specifically, a band group prompting (BGP) strategy is introduced to align the visual representation effectively at the level of band groups, which also facilitates a more adequate extraction of semantic information of different bands with textual information. In addition, a multivariate supervised matrix (MSM) based training strategy is proposed to alleviate the problem of positive and negative sample confusion by completing the supervised information. The experimental results demonstrate the effectiveness and superiority of the proposed data fusion framework.

The absence of temporal information in single-temporal satellite remote sensing images presents a substantial challenge for target state discrimination. In this letter, a pioneering Remote Sensing Airplane State Discrimination Network (RSASDNet) is introduced, by leveraging the relationship between targets and their backgrounds in single-temporal high-resolution remote sensing images. To facilitate the study, we take airplane state discrimination as an example, and a Remote Sensing Airport Panoptic Segmentation with Airplane States Dataset (RSAPS-ASD) is constructed. RSASDNet incorporates two key innovations: 1) a scene knowledge graph generation module that constructs scene knowledge representation by capturing spatial relationships between airplane instances and their surrounding environment (e.g., taxiways and hangars); and 2) a novel graph-image hybrid convolution discrimination module that synergistically integrates structural knowledge and spatial semantic information through dedicated dual-branch learning. The effectiveness of the proposed method is validated using RSAPS-ASD, with experimental results demonstrating that RSASDNet achieves an impressive accuracy of 73.95% in airplane state discrimination.

This paper explores a novel fuzzy fault-tolerant control scheme for hypersonic flight vehicles (HFVs) that accounts for parameter perturbations and system disturbances, thereby achieving flexible prescribed properties and designated-time convergence. In contrast to existing studies, this framework incorporates the more detrimental elevator stuck fault within the context of prescribed performance control (PPC). To mitigate the fragility issues associated with PPC in fault-tolerant control, we propose an innovative approach that integrates a fault sensing system along with a readjustment prescribed envelope. This is accomplished by transforming the original HFVs model into an imprecise pure feedback model, allowing for the design of low-complexity fuzzy controllers with minimal model dependence through the utilization of higher-order error functions and fuzzy logic systems. Finally, numerical simulations validate both the effectiveness and superiority of the proposed method.

Remote Sensing Panoptic Segmentation (RSPS) plays a crucial role in the intelligent interpretation of remote sensing data. Specifically, panoptic segmentation of airport scenes presents significant challenges, the varied shapes of terminal buildings with differences in appearance, size, scale, materials, and color make segmentation tasks highly complex. The intricate geometric shapes and varying scale parameters lead to the entangled object scales in the feature space, posing challenges for accurate segmentation. These factors collectively contribute to the complexity of panoptic segmentation for irregularly-shaped buildings. Current mainstream mask-based panoptic segmentation methods do not fully consider the importance of enhancing and integrating low-level and high-level semantic features. They lack the ability to decouple features, leading to misclassification and false negatives. To solve the above-mentioned problem of panoptic segmentation of irregularly-shaped buildings, this study designs a morphology-guided feature aggregation (MGFA) module to establish the correlation between low and high level semantic features at different stages. At the same time, a multi-order semantic decoupling (MOSD) module is designed to capture structural information at different scales and improve the network’s understanding of complex structural information. In addition, due to the lack of datasets for transportation hub scenarios in the RSPS task, we have collected the airport panoptic segmentation dataset (AP-RSPS) to promote the practical application of RSPS tasks. Extensive experiments on the AP-RSPS dataset and BSB Aerial Dataset show that the proposed MFGS2Net outperforms several SOTA methods.

Waverider vehicles (WVs) have the prominent advantage of long-distance and rapid delivery over traditional vehicles, but meanwhile, it also brings new challenges in control system design. Consequently, it is crucial to develop a reliable controller with good transient performance and steady-state accuracy for WVs. This article introduces a non-fragile prescribed performance control (NPPC) scheme that incorporates a precise flexible term for WVs subject to actuator constraints. The proposed NPPC scheme addresses the fragility issue of traditional PPC through two approaches: firstly, by designing a finite-time anti-saturation compensation system (FACS) with adjustable convergence time to accurately compensate for tracking errors; secondly, by utilizing the FACS as an error sensor and developing a precisely tunable prescribed performance flexible term. Moreover, uncertainty-estimation-free controllers are designed for WVs under actuator constraints based on the NPPC scheme and backstepping design approach. Additionally, the stability of the proposed controllers is proven through Lyapunov analysis while their advantages are further demonstrated and validated through comparative simulations.

In the realm of object detection, the age-old challenge of accommodating large variations in target scales, particularly in the intricate domain of remote sensing imagery, has long perplexed computer vision aficionados. Feature Pyramid Network (FPN) family, a widely-used stalwart, strives to tame this scale variation challenge by harmoniously fusing features across different levels. However, this typical feature fusion strategy often leads us astray. Noise introduction and feature smoothing problems due to different semantic information from high/low resolution feature maps, which results in semantic misalignment and inconspicuous gradient discrepancy between targets and background. This, in turn, leads to the difficulty in locating and distinguishing target from complex background in remote sensing images. In this paper, a GMM Feature Enhancement Module (GFEM) is proposed to address the problem by generating and enhancing feature of target with Gaussian Mixture Model (GMM), hence avoiding the gradient smoothing problem. Moreover, we introduce a generic feature fusion network named GFEM-FPN, elevating our approach to the next level. GFEM-FPN extracts multi-scale target enhancement features to enhance the ability of discriminating targets and background. The proposed methods are evaluated on NWPU VHR-10 and DIOR-R datasets, and the outperformance in results verify the effectiveness of the proposed method.

In the ship detection of the remote sensing images, cloud occlusion could blur the boundaries between ships and backgrounds, making it more challenging to distinguish them. Cloud occlusion can also result in partial or complete occlusion of target, making it difficult for models to detect ships in their entirety. Therefore, the use of cloud removal techniques is essential to enhance the accuracy and robustness of target detection. However, existing cloud removal processes are applied to entire images, providing limited improvements for specific object detection. In this letter, A Perlin Noise Based Thin Cloud Removal (PNBT-CR) Network is proposed for ship detection. The proposed algorithm introduces a Perlin noise mist mix module, which can improve the cloud removal effect of the network effectively. And it designed a Target-Oriented Structural Similarity (TOSS) loss function that enhances the network’s ability to boost the confidence of detected ships in the results. Experimental results demonstrate the efficacy of this approach in restoring texture details in ships and enhancing the accuracy of ship detection in remote sensing imagery. Moreover, the images processed using our method can have 89.86% SSIM compared to the original images, and when used for ship detection, there can be a maximum improvement of 11.7% F1-score.

It is time-consuming to collect a huge number of pixel-level annotations for accurately extracting buildings by deep neural networks. Supported by class activation map (CAM), weakly supervised semantic segmentation (WSSS) methods with image-level annotations serve as an efficient solution for building extraction. However, it is a great challenge to generate highquality CAM heatmaps for buildings from high-resolution remote sensing images. On one hand, image-level labels lack spatial information, resulting in partial integrity and hollow phenomenon for building extraction. On the other hand, complex backgrounds in remote sensing images can lead to inaccurate extraction of building boundaries. In this study, we propose a novel weakly supervised building extraction method called FlipCAM to deal with these challenges. The Flip module based on feature-level flipping augmentation is designed to improve the integrity of CAM heatmaps by fusing the original and flipped feature maps. In addition, by combining Flip module with slice and merge (SAM) module based on consistency architecture, FlipCAM is able to generate high-quality CAM heatmaps with both boundary fineness and internal integrity in an end-to-end manner, which also alleviates special difficulties for building extraction, including adhesions in dense buildings and confusions with background and shadows, providing reliable pixel-level pseudo masks for training segmentation network to extract buildings. Extensive experiments on three high-resolution datasets show that FlipCAM achieves excellent performance and outperforms other weakly supervised methods in terms of effectiveness and robustness capabilities. Our code is public at https://github.com/NJU-LHRS/FlipCAM-master.

Most existing polarimetric synthetic aperture radar (PolSAR) image classification methods are only valid under the assumption of identical imaging platforms and terrain categories for both training and test sets. To overcome this limitation, we propose a meta-graph representation learning (MGRL) method for PolSAR image classification with cross-platform and cross-category implementation. First, an integrated network is developed to learn the global-local representations of PolSAR images, which consists of a trumpet convolutional network (TCN) to learn the local scattering features of pixels and a graph convolutional network (GCN) for modeling the global structure of polarization information. Then, a comprehensive and transferable embedding of pixels is derived by collaborative optimization on multiple meta-learning tasks, which enables MGRL to recognize new classes not seen during training. Thus, the learned transferable representations can be quickly adapted to cross-platform and cross-category tasks with few labeled samples. Extensive experiments on several live airborne and spaceborne PolSAR datasets validate the effectiveness and advantages of MGRL over its counterparts.

Though deep learning methods have achieved the state-of-the-art performance for hyperspectral image (HSI) classification, they often highly rely on large amount of samples for training, and introduce few-shot challenge due to the lack of labeled samples. In this paper, a self-supervised method is presented for HSI classification in the few-shot scenario, where masked autoencoder is employed to reconstruct the masked bands in spectral domain for model pretraining with limited labeled sample, namely Spectral-MAE. The proposed method not only avoids the overfitting via the pretraining, but also provides the model’s ability for effective feature extraction while avoiding the high spatial redundancy. Experiments conducted verify the effectiveness of the proposed method for HSI classification in few-shot situation as compared with other methods.

High-performance target detection algorithms have been proposed for ship detection in Synthetic Aperture Radar (SAR) images in recent years. However, most of them are applied in the situation of single-polarization SAR images and rarely consider the important polarization information in SAR images. In this paper, a polarization-guided strategy is presented to improve the detection performance in single polarization SAR images by predicting polarization type. In which, an extra polarization-guided head is employed following the backbone network to guide the feature maps from the backbone to explore polarization information. Then, the feature map with the polarization information is fused with those from backbone network to enhance feature extraction in feature pyramid networks (FPN), and yields better detection performance. Experiments conducted demonstrate the effectiveness of the proposed method, which can be easily merged with different detectors.

Although introducing self-attention mechanisms is beneficial to establish long-range dependencies and explore global context information in the task of remote sensing image semantic segmentation, it results in expensive computation and large memory cost. In this letter, we address this dilemma by proposing a lightweight dual-range context aggregation network (LDCANet) for efficient remote sensing image semantic segmentation. First, a dual-range context aggregation module (DCAM) is designed to aggregate the local features and the global semantic context acquired by convolutions and self-attention, respectively, where self-attention is implemented easily by applying two cascaded linear layers to reduce the computational complexity. Furthermore, a simple and lightweight decoder is employed to combine information from different levels, in which a multilayer perceptron (MLP)-based efficient linear block (ELB) is proposed to yield a strong and efficient representation. Experiments conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen dataset and the Gaofen Image dataset (GID) prove that our LDCANet achieves an excellent trade-off between segmentation accuracy and computational efficiency. In particular, our method achieves 74.12% mean intersection over union (mIoU) on the ISPRS Vaihingen dataset and 61.42% mIoU on the GID with only 4.98-M parameter size.

Label assignment is a crucial process in object detection, which significantly influences the detection performance by determining positive or negative samples during training process. However, existing label assignment strategies barely consider the characteristics of targets in remote sensing images (RSIs) thoroughly, e.g., large variations in scales and aspect ratios, leading to insufficient and imbalanced sampling and introducing more low-quality samples, thereby limiting detection performance. To solve the above problems, an Elliptical Distribution aided Adaptive Rotation Label Assignment (EARL) is proposed to select high-quality positive samples adaptively in anchor-free detectors. Specifically, an adaptive scale sampling (ADS) strategy is presented to select samples adaptively among multi-level feature maps according to the scales of targets, which achieves sufficient sampling with more balanced scale-level sample distribution. In addition, a dynamic elliptical distribution aided sampling (DED) strategy is proposed to make the sample distribution more flexible to fit the shapes and orientations of targets, and filter out low-quality samples. Furthermore, a spatial distance weighting (SDW) module is introduced to integrate the adaptive distance weighting into loss function, which makes the detector more focused on the high-quality samples. Extensive experiments on several popular datasets demonstrate the effectiveness and superiority of our proposed EARL, where without bells and whistles, it can be easily applied to different detectors and achieve state-of-the-art performance. The source code will be available at: https://github.com/Justlovesmile/EARL.

Change detection is a critical task in earth observation applications. Recently, deep-learning-based methods have shown promising performance and are quickly adopted in change detection. However, the widely used multiple encoders and single decoder (MESD) as well as dual-encoder–decoder (DED) architectures still struggle to effectively handle change detection well. The former has problems of bitemporal feature interference in the feature-level fusion, while the latter is inapplicable to intraclass change detection (ICCD) and multiview building change detection (MVBCD). To solve these problems, we propose a new strategy with an exchanging DED (EDED) structure for binary change detection with semantic guidance and spatial localization. The proposed strategy solves the problems of bitemporal feature inference in MESD by fusing bitemporal features in the decision level and the inapplicability in DED by determining changed areas using bitemporal semantic features. We build a binary change detection model based on this strategy and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including ICCD datasets (CDD and SYSU), single-view building change detection (SVBCD) datasets (WHU, LEVIR-CD, and LEVIR-CD+), and an MVBCD dataset (NJDS). The experimental results demonstrate that our model achieves superior performance with high efficiency and outperforms all benchmark methods with F1-scores of 97.77%, 83.07%, 94.86%, 92.33%, 91.39%, and 74.35% on CDD, SYSU, WHU, LEVIR-CD, LEVIR-CD+, and NJDS datasets, respectively. The code of this work will be available athttps://github.com/NJU-LHRS/official-SGSLN.

Real-time positioning of flying aircraft based on hyperspectral remote sensing images is a novel application of satellite remote sensing. The complex aviation environment proposes huge challenges to accurately obtain features of flying aircraft from hyperspectral images (HSI) based on the traditional remote sensing image processing method. This paper proposes an automatic detection method for flying aircraft by Gaofen-5 (GF-5) HSI. This study firstly acquires the candidate target regions by detecting anomalies among the spectral inter-bands based on the imaging characteristics of the GF-5 remote sensing satellite sensor and the features of flying aircraft in the remote sensing image. The flying aircraft is then detected by confirming its features of a linear combination and proportional displacement. The experimental results demonstrate that the detection accuracy of flying aircraft can reach 99.9% under good weather conditions. Furthermore, the proposed method can effectively reduce the computational cost and realize the real-time detection of flying aircraft, which can be used to detect flying targets in large-scale HSI.

In this paper, a robust Student’s-T distribution aided One-Stage Orientation detector, namely TOSO, is proposed to address orientation target detection in remote sensing images. A one-stage keypoint based network architecture is used to avoid the complicated computation caused by rotation anchor boxes and two main contributions are proposed to enhance the performance. Firstly, a novel geometric transformation method is introduced to provide an orientation bounding box from its surrounding horizontal bounding box, so that the orientation angle is achieved by only regressing the geometric transformation parameters. Secondly, the Student’s-t distribution is used as a joint distribution to associate the classification task with the regression task, which are represented as Gaussian and inverse Gamma distributions, respectively. Experiments on two popular remote sensing public datasets DOTA and HRSC2016 confirm the improvement from our proposed TOSO detector.

Local Climate Zone (LCZ) classification is potentially popular because of its extensive applications. Recently, data from different remote sensors including synthetic aperture radar (SAR) and multi-spectrum are employed for LCZ classification. However, different bands in SAR and multi-spectrum are difficult to fuse because of their various physical properties. In this paper, an dynamic end-to-end fusion filter is proposed. Firstly, a convolutional neural network (CNN) based dynamic filter network (DFN) is introduced to integrate different bands in SAR and multi-spectrum data, which enhances the fusion accuracy by a flexible dynamic operation. Then the filter is used for feature extraction, hence improve the performance of the classifier. The proposed method is evaluated using Sentinel-1 and Sentinel-2 dataset and the improvement of accuracy shows the superiority of the proposed dynamic data fusion approach.

In this study, a Local Climate Zone (LCZ) classification framework is established using a Densenet based embranchment Convolutional Neural Network (CNN). Both synthetic aperture radar (SAR) and multispectral data are employed for feature fusion, specifically, considering about the difference in imaging mechanism between SAR and multispectral data, features from both resources are extracted in different branches separately according to the physical properties of each band. Significant accuracy improvement can be achieved when evaluate the proposed method by Sentinel-1 and Sentinel-2 dataset, and the comparison results show the superiority of the proposed embranchment CNN framework over the conventional methods.

Ship detection using synthetic aperture radar (SAR) plays a vital role in the wide area ocean surveillance. Especially the real-time ship detection and classification, significantly promotes the illegally operating ships monitoring performance. In this study, an on-orbit ship detection and classification method is developed for SAR satellite. An adaptive sliding window is developed to extract the water connected domain and propose the suspected ship target areas. The OpenSARShip dataset and manually selected non-ship slice images are adopted to train a deep learning model, which is applied to classify the proposed ship slice images into three types (cargo ship, other ship and false alarm). The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the detection accuracy improved from 88.5% to 98.4% and the false alarm rate mitigated from 11.5% to 0.7% compared with CFAR respectively. Meanwhile, the proposed method can achieved verification and testing accuracy of 97.2% and 93.4% respectively for ship type classification.

Snow cover in cold and arid regions is a key factor controlling regional energy balances, the hydrological cycle, and water utilization. Optical remote sensing data offer an effective means of mapping snow cover, although their application is limited by solar illumination conditions, conversely, SAR technology offers the ability to measure snow wetness changes in all weather. In the present study, a new approach using combined SAR and optical data has been developed for dry and wet snow cover recognition in mountain areas. In this method, RadarSat-2 interferometric coherence images and backscattering coefficient images are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth”, a dynamic thresholding algorithm was used to identify snow cover using interferometric coherence and local incidence angle images, and polarimetric target decomposition method was used to classify dry and wet snow cover. The classification results demonstrate that dry and wet snow cover extraction using this method can achieve 93.5% in snow-melt period.

Snow depth is one of the most important parameters for hydrological applications. SAR (Synthetic Aperture Radar) has the ability to monitor the surface deformation effectively, with a certain penetration and interference measurement capability. The refraction of microwaves in dry snow is shown to have a significant effect on the interferometric phase. According to this, snow depth estimation with repeat-pass InSAR (Interferometic SAR) measurement by using ENVISAT ASAR IMS products was proposed. Taken pediment plain in Manas river basin as study area, the optimal InSAR pairs (with and without snow) were chosen, and then the interferometric phase was calculated as a product of InSAR optimized processing. And local incident angle was used to improve the final snow depth estimated result as a substitution of satellite incident angle 23°. From snow depth estimated results between Jul. 2008 and Feb. 2009, it was pictured that the average snow depth is about 20cm in study area, which is consistent with the field survey results. Lastly, error evaluation was proposed to unreasonable estimation results, and also some reasons to cause inaccuracy were discussed: decoherence due to snow cover and atmospheric effects; two input parameters: local incident angle in SAR with snow may have changed because of snow distribution, and snow density varies as snow metamorphism and hierarchy in a whole study area.

Snow cover in cold and arid regions is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Interferometric synthetic aperture radar (InSAR) technology offers the ability to monitor snow cover in all weather. In this letter, a support vector machine (SVM) method for extracting snow cover based on SAR and optical data in rugged mountain terrain is introduced. In this method, RadarSat-2 InSAR interferometric coherence images are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” The analysis results indicate that the coherence in copolarizations is clearly correlated with the underlying surface type and local incidence angle. These two factors, combined with training samples from GF-1 wide field viewer data, were used to build an SVM to classify coherence images in HH polarization. The classification results demonstrate that snow cover extraction using this method can achieve mean accuracies of 83.8% and 77.5% in areas with low and high vegetation coverage, respectively. These accuracies are significantly higher than those achieved by the typical thresholding algorithm (72.7% and 69.2%, respectively).