Although significant advances have been made in the semantic segmentation of high-resolution remote sensing (RS) images, obtaining accurate pixelwise annotations remains resource-intensive. We propose SparseFormer, a credible dual-convolutional neural network (CNN) expert-guided Transformer model designed for semantic segmentation using point-level annotations to reduce this annotation burden. SparseFormer comprises three branches, where two CNN branches employ different attention mechanisms to encourage diverse outputs. To enhance the local consistency of pseudolabels, we introduce a pixel-adaptive refinement (PAR) module that dynamically refines CNN output probabilities by incorporating image information during training. A credible assessment is then performed to combine the CNN outputs, producing high-quality pseudolabels that supervise the CNN-Transformer hybrid branch. This hybrid branch integrates global representations with local features, achieving precise segmentation. To further strengthen the CNN branches, we introduce a knowledge distillation strategy that steadily feeds back information from the hybrid branch to CNN branches, mitigating overfitting risks caused by sparse supervision. SparseFormer employs credible assessment to reduce pseudolabel uncertainty, followed by continuous interaction and dynamic information enhancement among the three branches in an end-to-end training process. Extensive experiments on two benchmark datasets demonstrate that SparseFormer significantly outperforms state-of-the-art methods. Our code is available at:https://github.com/Yujia73/SparseFormer.

With the rapid development of hyperspectral image classification (HSIC) technology, its applications in geological exploration and environmental monitoring have become increasingly prominent. Recently, Mamba has garnered significant attention owing to its outstanding performance in long-range sequence modeling and linear computational complexity. However, Mamba still exhibits significant limitations in HSIC: first, it does not fully consider the hierarchical spatial-contextual representation and nonlinear spectral interactions in hyperspectral images; second, its sequential processing approach leads to the loss of spatial structural information and feature redundancy. In response, this study proposes a structure-enhanced spatial–spectral dynamic gating Mamba (SEDGM) that leverages the collaborative design of spatial and spectral gating Mamba mechanisms to extract and exploit key regional features of hyperspectral data. The spatial branch employs Hierarchical Gating Mamba to capture multi-directional pixel sequences and extract the hierarchical spatial features and their intrinsic relationships. In contrast, the spectral branch utilizes a Random Shuffled Gating Mamba to disrupt the fixed order of traditional spectral sequences and capture higher-order spectral couplings, effectively characterizing the cooperative variation patterns of spectral features. Both branches employ a dynamic gating mechanism that weights features based on sequence centrality, dynamically activating feature sequences. Additionally, shape-specific offset-aware attention is incorporated into each branch to enhance the structured features that were lacking in the Mamba sequences. Finally, a Spectral-Oriented Feature Review Module is incorporated to achieve dynamic feature fusion and optimized refinement. Experiments were conducted on four large-scale benchmark HSI datasets, with SEDGM achieving significant improvements in classification performance, validating the effectiveness of this approach in hyperspectral image classification tasks. The code is available at https://github.com/shuai2023-hash/SEDGM.

The digital elevation model (DEM) is a digital representation of the surface elevation, but it contains elevation errors arising from vegetation coverage and terrain undulation. Spaceborne LiDAR, with its large-scale and high-precision elevation measurement capabilities, can effectively correct these errors and has become an important means to improve the elevation accuracy of DEM. In this study, a hybrid incremental regression (HIR) model based on the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) laser altimetry data is designed to correct large-scale DEM elevation errors. The model combines a neural network and a decision tree model to efficiently fit the elevation error through stepwise regression, which can be applied to different terrains and landforms over large areas. In the experiment, first, the proposed model was compared with four classic models [multiple linear regression (MLR), random forest (RF), backpropagation neural network (BPNN) and light gradient-boosting machine (LightGBM)] in seven different landform areas, and the results showed that our model was better than other models in terms of accuracy improvement and was applicable to various terrains. Then, the model was applied to China, and the higher precision, large-scale DEM datasets were produced. Compared with the original DEM, the root mean squared error (RMSE) and mean absolute error (MAE) of the optimized DEM were reduced by 1.603 and 1.453 m, respectively. The differences in accuracy under different slopes, land cover types, and vegetation heights were also analyzed, and it was found that the enhancement effect was the best in the high-vegetation cover area. Finally, it is validated using three regions of high-precision validation data, and the results showed that the RMSE had improved to varying degrees, ranging from 0.193 to 2.853 m.

Accurate crop identification is crucial for agricultural trade, market risk management, and food security. Current research on automatic interannual sample extraction for crop mapping often emphasizes multisource feature fusion but overlooks the importance of feature distance differences in crop seeding processes. This study focuses on crop mapping in the Hetao Plain from 2020 to 2023, using high-resolution (HR) Sentinel-1 and Sentinel-2 remote sensing data. We introduce a method called BGSI-DFF-WMRF, which combines the bidirectional global selection index (BGSI) and distance feature fusion (DFF) under the interannual weaving net month probability random forest (WMRF). BGSI captures the coupling between phenological and spatial factors necessary for crop growth in different regions under feature fusion. Additionally, sample migration under feature fusion enhances the accuracy and representativeness of sample points across different years. WMRF integrates a monthly classifier with multisource feature distance interpolation. The BGSI-DFF-WMRF method achieved over 82% classification accuracy for crops like wheat, corn, and sunflower in the Hetao Irrigation District (HID) region, with an accuracy of 91.81% in the western area. Field samples from 2023 were successfully applied to previous years (2020–2022) through feature fusion expression (FFE) transfer. The method outperformed existing products and local statistical data, particularly for corn, demonstrating high accuracy and robustness in crop mapping. Coupling spatio-temporal factors enhances large-scale crop identification and holds great significance for the advancement and widespread adoption of large-scale crop identification techniques.

Semi-supervised semantic segmentation has gained considerable attention due to its ability to leverage large amounts of unlabeled data to enhance model generalization. Although increasing the amount of unlabeled data ideally enhances performance, reality contradicts this notion. Research indicates that the reasons for performance degradation span the entire semi-supervised learning process. These include the inconsistent distribution of unlabeled and labeled data at the data level, the susceptibility to interference from unknown categories at the model level, and the semantic drift problem caused by the training strategy. This article refers to these challenges as the “full-cycle” challenge. To address these issues, we propose a full-cycle framework. At the data level, we develop a consistency-based method, beginning with unlabeled data screening, to prioritize unlabeled data that closely aligns with the labeled data distribution in training. At the model level, we introduce a consistency-based semi-supervised semantic segmentation model capable of identifying unknown category regions and distancing their features from known category features, reducing the interference of unknown classes. At the training strategy level, we adopt a progressive approach that gradually incorporates data from simpler to more complex cases. This strategy improves the model’s adaptability to potential data changes, ensures stability, and improves overall generalization performance. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches, validating its effectiveness in addressing the full-cycle challenge.

The clustering assumption is widely adopted in semi-supervised semantic segmentation methods. However, this assumption heavily relies on high-quality feature representation, leading to learning and cognitive biases if it does not hold. Learning bias entails the potential overfitting of labeled data, leading to capturing local features and consequently misclassifying semantic categories. Cognitive bias indicates the network’s susceptibility to interference features. Recently, a consistency-based mechanism has been proposed to address these biases. By subjecting unlabeled data to diverse weak perturbations, it breaks original clustering features, compelling the model to learn more robust and generalized representations. However, when processing complex remote sensing images, these weak perturbations often prove ineffective in the later stages of training. To address this, we propose a bias correction framework (BCF). The BCF begins with a feature consistency enhancement module (CEM) that guides the student model to learn feature representations with greater generalization capability. In the feature decoding stage, we introduce a multidecoder structure with weakly orthogonal weights to maximize feature differences, thereby further reducing learning and cognitive biases. In addition, to improve the confidence of pseudolabels and enhance consistency learning, we design a multidecoder teacher model based on symmetrical knowledge transfer, allowing the diverse and multiangle information learned by the student model to be transferred to the teacher model. Extensive experimental results show that our method significantly outperforms state-of-the-art methods on the ISPRS Potsdam and Vaihingen datasets.

Large-ratio cloud occlusion significantly hampers the utilization of high-resolution remote sensing imagery. The existing reconstruction methods (1) overlook the problem of reconstructed and composite images sharing high-and low-level semantic and visual attributes in non-reconstructed regions, exacerbating the pronounced boundary effects; (2) neglect appearance discrepancies between reconstructed and non-reconstructed regions, leading to spectral degradation, and texture loss; and (3) overlook the problem of reconstructing large-ratio missing information. To address these issues, a global and local dual fusion network is proposed in this study for large-ratio cloud occlusion removal in high-resolution remote sensing images. The global foreground–background aware attention module tackles shared high-level semantic features, whereas the local visual feature enhancement module addresses appearance differences. The global and local dual fusion network combines the Sobel and reconstruction loss functions for effective reconstruction by employing a two-stage fusion strategy. Compared to the classical recurrent feature reasoning network, spatiotemporal generator network, spatial-temporal-spectral convolutional neural network, and bishift network, the proposed model demonstrates superior quantitative and visual reconstruction outcomes for the 40%, 50%, and 70% missing ratios of Gaofen-1 (2 m).

The swath of the conventional spaceborne synthetic aperture radar (SAR) is parallel-to-orbit, making it inefficient to observe long curved terrain, like coastlines, railways, etc.. Imaging long curved terrains with the terrain matching (TM) curved swath is a promising technique for efficient data acquisition. The key feature is the employment of a long curved swath matching with the orientations of the long curved terrains. This paper reports the first demonstration of the spaceborne SAR TM curved imaging with the LJ2-01 satellite. A TM curved swath of 161.7 km is imaged with an azimuth resolution of 0.6 m. The main technical contributions are: First, a new electrical-mechanical-combined beam control method is proposed to achieve uniform azimuth resolution; Second, a new non-uniform pulse repetition frequency sequence is used to mitigate the data loss caused by the violent spatial variation of the slant range; Third, a new swath-adaptive sub-aperture time-domain imaging algorithm is proposed for efficient TM curved swath imaging. These innovations contribute to successful data acquisition and imaging of the spaceborne SAR TM curved imaging with the LJ2-01 satellite.

Semantic segmentation of high-resolution remote-sensing images (HR-RSIs) focuses on classifying each pixel of input images. Recent methods have incorporated a downscaled global image as supplementary input to alleviate global context loss from cropping. Nonetheless, these methods encounter two key challenges: diminished detail in features due to down-sampling of the global auxiliary image, and noise from the same image that reduces the network’s discriminability of useful and useless information. To overcome these challenges, we propose a truncation compensation network (TCNet) for HR-RSI semantic segmentation. TCNet features three pivotal modules: the guidance feature extraction module (GFM), the related-category semantic enhancement module (RSEM), and the global-local contextual cross-fusion module (CFM). GFM focuses on compensating for truncated features in the local image and minimizing noise to emphasize learning of useful information. RSEM enhances discernment of global semantic information by predicting spatial positions of related categories and establishing spatial mappings for each. CFM facilitates local image semantic segmentation with extensive contextual information by transferring information from global to local feature maps. Extensive testing on the ISPRS, BLU, and GID datasets confirms the superior efficiency of TCNet over other approaches.

Considering that the atmospheric refraction and aberration of light (AOL) bend the path of light and thus affect the geolocation accuracy, a rigorous imaging geometric model with corrections of atmospheric refraction and AOL is proposed to improve the accuracy of geolocation for GaoFen-7 (GF-7) images without ground control points (GCPs). The correction of atmospheric refraction is calculated using a simplified two-layer atmospheric refraction model, while the correction of AOL is calculated using the satellite altitude and velocity. Then, the rigorous imaging geometric model is refined with both corrections. A total of 68 scenes of GF-7 satellite backward images in ten regions with different roll-off-nadir angles are selected to compare and detect the variation of accuracy with and without the corrections. The results show that the average geolocation accuracy is improved by 0.14 m in object space, and about 0.20 pixels in image space if the corrections of atmospheric refraction and AOL are taken into the geometric model. The average improvement of RMSEs is about 1.00 pixels and the ratio of improved scenes reaches 88.89% when the roll-off-nadir angle is larger than 15°, whereas there is no need to consider atmospheric refraction and AOL if the off-nadir angle is smaller than 5°. The methodology and results can provide support for the need for corrections for high-precision positioning and the promotion of GF-7 imagery.

The exterior elements of the linear array satellite will change over time, resulting in significant degradation of the geometric positioning accuracy of the image. It is necessary to conduct geometric calibration of the camera in a quick and timely manner. Therefore, this study proposes a geometric calibration method through a star observation which satellite could be implemented at any position in orbit. The linear array camera point to the deep space and aim at the star for shooting. The star coordinate extracted from the image was regarded as the control point to calibrate the exterior elements of the camera. Then the geometric positioning accuracy of the image is improved. In this study, several Jilin-1 linear array satellites have been verified. The satellites with different launch times were used for star observation, and the geometric positioning accuracy was better than 50 m after the calibration by star observation method.

Owing to changes in the space thermal environment, the optical axis angle between the camera and star tracker (Cam-ST) changes as well, resulting in regular low-frequency attitude errors and a decline in the geometric positioning accuracy of the images. In this study, a star-observation-based low-frequency attitude error correction method is proposed. First, the star was used as the control point to be observed by satellite from different latitudes in an orbit. The star information is extracted using a priori attitude to obtain the camera pointing in inertial coordinates. Second, combined with the optical axis pointing value of the star tracker, the angle change between the camera and two star trackers (Cam-2STs) was calculated. A polynomial model was used to fit the angle change, and the fitting model and camera optical axis pointing compensation algorithm were used to compensate for the camera optical axis. Third, the regular low-frequency attitude error of the camera was realized. The experiment used 20 groups of star observation data from two missions of the Jilin-1 07 video satellite. After compensation, the camera pointing maximum root mean square error was reduced from 30.08" to 7.70", theX-axis attitude error was reduced from 13.26" to 7.09", and theY-axis from 29.48" to 7.36". The geometric positioning accuracy of Jilin-1 ground images was 25.63 m after correction. The experimental results showed that the proposed method effectively improved the attitude measurement accuracy. This method can thus effectively improve the direct geometric positioning accuracy of a single image.

Change detection and extraction of buildings based on convolutional neural networks (CNNs) have made encouraging progress in the remote sensing community. Although these two tasks are different in objective and application scenarios, both focus on building objects. However, previous methods were accustomed to considering these two tasks separately, and the change detection task suffered from the precondition that bitemporal labeled images were used as paired supervision signals. In this study, we propose a stable prototype guided single-temporal supervised learning framework (PGLF) as a joint solution for building change detection and cross-temporal extraction by exploring two cores: knowledge commonality and task specificity. For knowledge commonality, we introduced a multi-prototype representation module (MPRM) to generate stable building prototypes from support foreground features and designed a prototype to query feature adaptive fusion (PQAF) module to suppress background noise and extract discriminative building features in a way that support prototypes-guided query feature enhancement. For task specificity, we designed a multiscale spatiotemporal interaction module (MSTI) to capture bidirectional change features with strong spatiotemporal correlations. Besides, we developed a pseudo bitemporal image pair construction method to improve the performance of building change detection and cross-temporal extraction under single-temporal supervision signals. PGLF was trained on pseudo bitemporal labeled image pairs and tested on the public aerial WHU building dataset and proposed satellite SPO building dataset. The comprehensive experimental results demonstrate the superiority of the proposed method.

Synthetic aperture radar (SAR) has evolved into an essential Earth observation technique. Geometric quality is one of its most fundamental elements to ensure subsequent applications. Excellent localization accuracy requires comprehensive error compensation and calibration. While traditional calibration work is time-consuming and depends on the test sites. Besides, the available calibration test sites are scarce and unevenly distributed for certain SAR satellites. It is, hence, necessary to research how to make geometric calibration without ground control points (GCPs) for SAR. This study proposes a method utilizing the constraints of symmetric geometry that could cancel out plane error symmetrically, and then calculates calibration constants combined with external DEM. Experiments demonstrate the calibration accuracy of the proposed way is approximately 0.3 m along range direction when all constraints are met, and the discrepancy between the proposed method and that using GCPs are analyzed. Generally, the method can achieve fast, labor-saving, and normalized calibration work for SAR images in the absence of available control data.

The imaging process of optical remote sensing images are easily affected by external conditions. Therefore, remote sensing images under different imaging conditions often show color differences, resulting in feature distribution differences between the source and target domain, hindering the migration of semantic segmentation models between domains. Currently, most domain adaptation methods are for single-source and single-target domains. Here, we proposed a novel and concise method, coined MDANet, for the adaptation of patch images of multi-source and multi-target domains and for reducing the distribution differences of different patch images by projecting them onto the virtual center of a mixed-domain. MDANet is a lightweight and self-supervised network that can be grafted with any semantic segmentation model. Our method significantly improved the segmentation accuracy of semantic segmentation models and showed higher stability and competitiveness than existing methods.

The ice, cloud, and land elevation satellite-2 (ICESat-2) is equipped with a photon-counting laser altimeter system and demonstrates outstanding ability to measure elevations in the ever-changing earth. However, the ICESat-2 data contain several noise photons affected by solar returns, and there are no reference data of signal or noise photons for evaluating the performance of denoising algorithms. In this letter, we propose a self-adaptive denoising algorithm based on a genetic algorithm (SADA-GA) for the ICESat-2 data, which uses the real-coded genetic algorithm to adaptively search for the global optimal denoising parameters in different data sets. The SADA-GA addresses the limitation of the selection method of the two parameters K and T in the localized statistics-based algorithm that normally cannot be applied to different data sets. To evaluate the algorithm performance, we created an ICESat-2 data set named WHU-PCL and compared the SADA-GA with two classic methods. The qualitative and quantitative analyses showed that our method can extract signal photons more efficiently from different ICESat-2 data sets and achieve the$F$value of 0.99 in nighttime data. In addition, we analyzed the factors that affect the SADA-GA performance and found that the signal-to-noise ratio (SNR) is the most important parameter.

Geolocation is one of the key synthetic aperture radar (SAR) image processing procedures in SAR-based remote sensing applications. The conventional SAR imagery geolocation model is based on the range-Doppler (RD) equation, which is derived from the Fresnel approximation of SAR. Currently, there is no other rigorous geolocation model for modern high-resolution SAR; however, this approximation may not be fully applicable for various modern SAR imaging modes and algorithms. This study aims to develop a novel SAR image geolocation model based on the SAR range history (RH) equation, which is derived from the basic SAR backscattered signal property. Moreover, a new geometric calibration model is presented for RH-based location. Experiments based on real spaceborne SAR data are presented as examples, and the location accuracy of RH and RD location models are compared. The results show that the location accuracies of the RH and RD models are consistent under the existing SAR processing framework, but those for the RH model demonstrate better performance in the azimuth direction. In theory, the proposed RH model can be used for SAR image products generated from complex modes and non-Fresnel-approximation-based imaging algorithms, making it suitable for future SAR systems.

Noticeable striping artifacts in collected satellite imagery, caused by the inconsistency of pushbroom sensor detector responses, degrade the image quality. Relative radiometric calibration aims to calibrate inconsistencies in terms of detector responses, thereby eliminating detector-level striping artifacts. Yaw calibration has become the preferred imagery-based calibration method for satellites without on-board calibration equipment, owing to its high accuracy and convenience. However, it often relies on a large uniform field on the Earth’s surface and is a linear calibration method. This method does not consider acquisition-related geometric errors associated with the sensor, resulting in reduced calibration accuracy. In this study, we propose an improved method for yaw calibration, which accounts for the geometric distortion of sensor imaging and does not require a uniform field. A fast geometric-positioning algorithm was used to remove geometric distortion, followed by high-precision extraction of the calibration reference for each sensor’s detector. The dynamic range of the sensor calibration was extended, followed by the calibration of the sensor nonlinear response model. Our results based on Yaogan-25 images suggest the following: 1) the improved method effectively eliminates the “sawtooth” caused by the yaw calibration method without considering the geometric distortion and 2) it outperforms other imagery-based calibration methods with respect to visual destriping effects such that the root-mean-square deviations of the corrected imagery experienced a decrease of 0.65 percentage point, compared with that of the statistical method, and 0.17 percentage point for the yaw calibration. Such improvements will promote high-quality applications of remote-sensing images.

GaoFen-3 (GF-3) is a C-band multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes. However, its initial positioning accuracy remains unsatisfactory, thereby hindering its use for large-area surveying and mapping. This study proposes a block orthorectification method without ground control points (GCPs) using the GF-3 Fine strip II (FSII) mode. To address the challenges with the accuracy and efficiency of this method, an integrated block orthorectification method was developed to conduct integrated processing of large-scale GF-3 satellite images without GCPs. Geometric calibration was used to improve the absolute positioning accuracy of each SAR image. Then, several tie points (TPs) were extracted using the SAR scale-invariant feature transform (SIFT) operator. A parallel matching strategy was used in the block images registration. The block adjustment model was constructed to solve the orientation parameter of all SAR images. The experimental results of 1,468 GF-3 images of China’s entire land area show a TPs root-mean-square error of 0.724 pixel and 8.014 m for the independent checkpoint, suggesting that the proposed method can effectively improve the geometric accuracy of GF-3 satellite images and demonstrate the feasibility of large-scale SAR mapping without GCPs.

Hyperspectral remote sensing images (HSIs) have been applied in urban planning, environmental monitoring, and other fields. However, they are susceptible to noise interference, such as Gaussian noise, stripe, and mixed noises, from various factors in the imaging process, which greatly limits their applications. Although previous efforts to improve HSI quality have achieved remarkable results, there are still many challenges to be solved. To avoid the poor generalization ability and improve the stripe removal performance of the network in real scenarios. In this paper, we proposed a novel deep learning model (Translution-SNet) for HSI stripe noise removal based on a semi-supervised training strategy that applies a convolution and transformer for feature extraction. Moreover, we used an unbiased estimation method to calculate the loss function of the unsupervised part from noisy data without a clean image. The semi-supervised method improved the ability of Translution-SNet to deal with various complex stripe noises during stripe removal and strengthened its robustness and generalization ability. Our experimental results showed that Translution-SNet could robustly handle stripe noise of images with different loads and achieve satisfactory results, proving its feasibility and effectiveness. In addition, Translution-SNet showed good generalization ability.

Remote sensing images, especially hyperspectral images (HSIs), are extremely vulnerable to random noise and stripe noise. As a key aspect of HSI data quality improvement, stripe noise removal has always been a pervasive issue in remote sensing image processing. Convolutional neural networks have been applied for HSI data destriping. However, the existing methods lose the stripe-free component of the original image to a certain extent. These models also ignore the global spatial context of images and the correlation between spatial information and spectral information. Therefore, we propose a novel destriping convolutional network to overcome the problems with the existing methods. Octave convolution is used to extract cross-frequency features, and separate and compress the low-frequency information of the images, while dilation convolution (Dila-Conv) is used to reduce the amount of required calculation and also preserve the key image information. In addition, Dila-Conv can expand the receptive field to obtain multiscale features. Finally, a cross-channel enhanced spatial–spectral feature fusion module is used to acquire and integrate spatial context information and interchannel dependencies on a global scale as auxiliary information so that the network model can learn and pay attention to key feature information, specifically, “what to look for” and “where to look at,” which can facilitate the distinction between stripe and stripe-free components. Experimental results obtained using multiple datasets demonstrated that the proposed method can outperform the existing comparable methods and can produce satisfactory results in terms of visual effects and quantitative evaluation.

The forward prediction of mining subsidence in coal mining areas is key to evaluating mining risk and improving mine management plans. At present, the conditions that ensure successful application of the time function method to predict future subsidence in coal mining areas remain poorly known, and a prediction method suitable for large-scale subsidence prediction during coal mining has not been established. Based on the characteristics of logistic model and simulation experiments, we determined its predictable conditions for coal mining subsidence prediction. We propose a small baseline subset-synthetic aperture radar interferometric (SBAS-InSAR) coal mining subsidence prediction method with a predictable dynamic range based on the predictable condition obtained. The method uses time-series subsidence data obtained by SBAS-InSAR as the fitting data. The logistic model parameters are obtained pixel-by-pixel via the Levenberg–Marquardt (LM) algorithm. The predictable range is subsequently determined based on the predictable condition. Finally, future subsidence in the predictable range is predicted. The methodology was tested in two coal mining areas in Inner Mongolia–one a single working face mine and one a parallel double working face mine. The predicted results agree well with the InSAR monitoring results. The average root mean square error (RMSE) of the predicted results was 0.0119 m. In addition, we used the Knothe model to conduct comparative experiments without considering predictable conditions. The results reflect the advantages of our proposed method and the necessity of predictable conditions. The new prediction method is beneficial for risk assessment and coal mining planning.

High-resolution synthetic aperture radar (SAR) satellites have become an important way to observe the earth. However, the geometric positioning accuracy of SAR satellite images across different times and spaces is an important factor affecting the realization of global remote sensing applications. In this study, a multimode hybrid geometric calibration method that incorporates an atmospheric propagation delay correction and that can be used to detect systematic errors affecting the geometric positioning accuracy of SAR satellites is described. The spatiotemporal variation reasons of geometric positioning error sources for spaceborne SAR are then analyzed. Finally, the stability of geometric positioning accuracy is evaluated using the described method on data extracted from Yaogan-13 (YG-13) SAR satellite images with long time series and multiple test areas in China. The results reveal that during the study period (2015–2017), the geometric positioning accuracy of the YG-13 SAR system was relatively stable and is better than 3 m regardless of the spatial distribution, after removal of systematic pulse-dependent slant range errors and atmospheric correction. Furthermore, the validation results provide a reference for the design of SAR satellite systems, the establishment of calibration periods, and quantitative remote sensing application.

The high-precision geometric positioning of satellite images is the basis for the geometric processing of remote-sensing images and acquisition of various geospatial information. It is an important premise for the wide application of high-resolution remote-sensing satellite images. To correct systematic biases in rational function models (RFMs), many compensation methods for system errors inherent of RFMs have been proposed. Thus far, the bias compensation model (BCM) is the most widely accepted method under rigorous conditions, namely narrow camera field, small off-nadir angle, and small attitude error. However, research studies on the compensation effect of the BCM under weak conditions (wide camera field, large off-nadir angle, or large attitude error) are still lacking, and some researchers commented that the BCM is inapplicable under weak conditions in the absence of experiments. This letter analyzes the effect of position and attitude errors on orientation accuracy in the image space. An experiment was conducted using data from the Gaofen-1 (GF-1) wide-field-view-4 (WFV-4) sensor to compare the proposed analysis with the traditional analysis, and the results obtained using the BCM under weak conditions were found to be consistent with our analysis rather than the traditional analysis. This confirms that the BCM can also be used under weak conditions.

Optical satellites are affected by factors such as seasonal and atmospheric variation, illumination, and sensor distortion. Thus, satellite images covering large-scale area often show conspicuous color differences, resulting in poor color continuity of the mosaicked satellite image. This study proposes a novel combined model color correction (CMCC) method for high-resolution optical satellite images, which constructively combines a defogging model with a radiation correction model. First, this study analyzed the feasibility of using easily available low-resolution satellite images as external references to correct the color of high-resolution images and describes the selection criteria for external references. Second, considering the negative effects of atmosphere on the color and clarity of remote sensing images, we proposed an optical satellite image enhancement method, which is based on the content characteristics of remote sensing images and the dark channel prior defogging method. Finally, we designed a two-stage color correction process: 1) correcting the color of downsampled images via low-frequency modeling and replacement and 2) mapping the color of downsampled images to original images through local modeling and super-resolution color correction. Furthermore, this study proposes an indicator of quality considered mean absolute error (QCMAE) for quantitative evaluation of the color correction result. We selected 328 Gaofen-1 (GF-1) high-resolution images for the experiments. Visual effects and statistical results of images after being processed by the proposed CMCC are both superior to the three state-of-the-art methods, which verifies the effectiveness and reliability of the proposed method.

Synthetic aperture radar (SAR) images reveal severe geometric distortions especially in the mountain area, such as layover, which is caused by imaging characteristics of SAR itself and terrain undulations. The layover phenomenon greatly limits the application of SAR images. This article proposes a layover compensation method for regional spaceborne SAR imagery without ground control points (GCPs), which is mainly improved from two aspects. First, a method based on rational function model (RFM) to determine the layover range is proposed. Second, based on geometric calibration and block adjustment, the processing flow is optimized to generate digital orthophoto map (DOM) which greatly eliminated the influence of layover. The proposed method was applied to Chinese Gaofen-3 (GF-3) SAR regional images, including the ascending and descending track stacks. The result showed that 84.5% of the layover pixels on the regional DOM were compensated, which verified the effectiveness and feasibility of the method.

Optical remote sensing satellite usually adopts the method of photographing calibration field to achieve calibration of internal and external orientation elements of optical camera. However, as time goes on, or as the latitude changes, the accuracy of satellite positioning after calibration will decline. The traditional calibration method requires satellite passes calibration field and is difficult to achieve globally, so it's hard to be realized that keeping the global positioning accuracy high. The purpose of this study is to explore a more feasibility calibration method of interior orientation elements in optical satellites. That is, by photographing stars, the stars are used as control points to calibrate the camera's internal orientation elements. In this study, a camera calibration method based on multi-frame star map was proposed. The star maps were captured by the star camera of Ziyuan-3 02 (ZY-3 02). Final result proved to be efficient to offer sufficient information for interior orientation elements calibration purpose.

Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications.

This article proposes a novel fusing filter algorithm based on a surface variation anisotropic diffusion (SVAD) filter and a ratio image filter to achieve good speckle reduction and edge preservation. The proposed algorithm can be divided into three steps. First, the proposed SVAD filter effectively calculates the diffusion coefficient of each pixel to obtain filtering results on different scales. Second, the proposed ratio image filter obtains a new denoising result that can effectively recover some details lost with the SVAD filter. Then, the two filtering results are fused to obtain the final despeckling result. Furthermore, the effects of the weighting coefficients of the fusion processing and the number of iterations of the ratio image filter on the final filtering results are analyzed. The proposed algorithm is effectively evaluated by conducting some experiments on the added noise image and real synthetic aperture radar (SAR) images. The experimental results confirm that the proposed method can not only significantly reduce speckle but also effectively preserve the edge information of images.

Through the use of video technology, satellites can detect dynamic targets and analyze their motion characteristics. Target tracking can extract dynamic information about key ground targets for target monitoring and trajectory prediction by satellite video. Tracking algorithms are affected by target motion characteristics, such as velocity and direction, as well as background characteristics, such as illumination changes, occlusion, and background similarities with the target. However, these problems are seldom studied with satellite video cameras. Current algorithms are unsuitable for satellite video because of the poor texture and color features of the target in satellite video. Therefore, in this article, we enhance target tracking for satellite video technology using two aspects: 1) sample training strategy and 2) sample characterization. We establish a filter training mechanism for the target and background to improve the discrimination ability of the tracking algorithm. We then build a target feature model using a Gabor filter to enhance the contrast between the target and background. Moreover, we propose a tracking state evaluation index to avoid tracking drift. Tracking experiments using nine sets of Jilin-1 satellite videos show that the proposed approach can accurately locate a target under weak feature attributes. Therefore, this article contributes to more robust tracking using satellite video technology.

Aiming at the imaging problem of regional target in emergency surveying and mapping, a multi-objective optimization model(MOOM) is proposed, which takes the imaging lateral swing angles of satellite as decision variables and takes the maximum coverage rate of regional target and the minimum number of holes as objective functions. Aiming at the two key problems of evaluation function calculation and multi-objective model solving, Vatti algorithm and NSGAII algorithm are used to solve them respectively. Finally, STK simulation data are used to verify the feasibility of the optimization method.

The background noise of the photon counting laser point cloud data is large, and the distribution of point cloud density is uneven. In this paper, a point cloud denoising algorithm based on local distance weighted statistics is proposed to solve the problem that the noise point and non-noise are difficult to distinguish when the density of point cloud is low. By adding the weight function, the density difference between noise points and non-noise points is increased when the density of point cloud is low. This method can effectively remove the noise points and thus extract a continuous and complete effective point cloud. Besides, this paper compered the proposed method with the traditional point cloud denoising algorithm based on local distance statistics to verify the efficiency of the proposed algorithm. The results show that the algorithm in this paper has more advantages in dealing with the uneven density of laser point cloud.

The replacement of the rational function model (RFM) by the rigorous sensor model (RSM) has been studied extensively and verified with many types of sensors and remote-sensing applications. However, relatively less research has been conducted on recovering RSM from RFM, and the few relating techniques can only be applied in specific circumstances. This paper proposes a novel linear method to obtain the position, attitude, and interior orientation (IO) elements of satellites based on the orientation information of the rays implied by the RFM. Instead of resection, forward intersection is used to solve for position, and an equivalent body coordinate system is introduced to overcome the strong correlation between the attitude and IO. The orientation information of the rays implied by the RFM is used to calculate the IO pixel by pixel. Experiments using the Ziyuan 3 panchromatic nadir sensor show that this method can recover the exterior orientation and IO elements effectively.

Attitude models play a prominent role in the geometric processing of high-resolution satellite imagery (HRSI). Because of the high accuracy of the matching algorithm, attitude oscillations can occur in HRSI. Various methods for correcting this attitude oscillation with parallax observations have been proposed. However, few researchers have attempted to model the oscillation from the attitude records or have taken noise into consideration. In this paper, a penalized spline-based attitude model is proposed, which can model the oscillation with piecewise and continuously differentiable polynomials and smooth out the attitude noise with a penalty function. The balance between the fitting accuracy and noise smoothing is controlled by a penalty parameter, which is estimated by generalized cross-validation. Given that the attitude error introduces distortions into sensor-corrected images, the band-to-band registration of multispectral images is used to validate the attitude model. Five multispectral data sets captured by ZiYuan-3 are used to demonstrate the effectiveness of the proposed method. Compared with third-degree polynomials and cubic spline interpolation, the penalized spline model delivers the best performance by limiting the misregistration caused by the attitude model to within 0.1 pixels.

The Ziyuan-3 (ZY-3) satellite was designed to satisfy 1:50000 scale mapping requirements. This study uses 556 images obtained by ZY-3, from June 1 to October 31, 2013, covering an area of 3 500 000 km2in midwestern China. A total of 900 check points measured by a global positioning system were also used to conduct the planar accuracy verification. The experimental results show that the ZY-3 nadir sensor calibration images achieved a planar root mean square error (RMSE) of 10.8 m without the use of ground control points (GCPs). In addition, the verification of vertical accuracy employed 12 ZY-3 stereo image pairs distributed over an area of 14 000 km2around Taiyuan in the Shanxi Province of China, and a Digital Elevation Model with 0.5-m vertical accuracy was used for reference and validation. The vertical accuracy of forward interaction and stereo-extracted Digital Surface Model (DSM) from the stereo images were both validated without GCPs. The experimental results demonstrate that the overall vertical RMSE of the forward intersection was 6.58 m; it was 5.21 and 7.07 m for flat and mountainous terrain, respectively. Moreover, the overall vertical RMSE of DSM was 5.56 m; it was 4.37 and 5.69 m for flat and mountainous terrain, respectively. It can be seen from the experimental results of planar and vertical accuracy verification that ZY-3 imagery is able to satisfy the requirements of 1:50000 topographic mapping in China without using GCPs.

A rational function model (RFM) can be used directly to convert the relationships between image coordinates and object space coordinates without using any physical imaging parameters (such as satellite position and attitude). Thus, RFMs facilitate versatility and high security during geometric processing of optical satellite imagery. Increasingly, RFMs are offered to users as the basic geolocation model for further geometric processing by imagery vendors. However, imagery vendors might perform inadequate in-orbit geometric calibrations, or the calibrated geometric parameters might not be updated in a timely manner. Thus, the RFMs may suffer from high distortion due mainly to interior errors (such as lens distortion). Using the radiometric correction products of Ziyuan1-02C panchromatic and multispectral sensor as examples, the present study addresses the compensation of systematic errors in RFMs. An undistorted RFM can be generated after calibrating the interior error compensation model once, before high-accuracy registration between the panchromatic imagery and multispectral imagery can be achieved using the undistorted RFM. Experimental evaluations based on the positioning accuracy using a few ground control points (GCPs) with an undistorted RFM matched the accuracy of the GCPs. In addition, our approach greatly improves the accuracy of registration (which surpasses 0.7 panchromatic pixels) between panchromatic and multispectral imagery.

Given that long strip satellite images have the same error distribution characteristics, we propose a block adjustment method for satellite images based on the strip constraint. First, the image point coordinates are calculated in the strip image coordinate system based on the offset value of the adjacent image. Second, the rational function model (RFM) of the strip image is regenerated using the RFM of single images, and the compensation grid is also generated. Third, block adjustment of the strip image is implemented based on the RFM with an affine transformation parameter. Finally, the affine transformation parameters of single images are recalculated using the affine transformation parameters of the strip image. Experiments using ZY-3 satellite images showed that block adjustment of satellite images based on a strip constraint (strip adjustment) can produce better results than block adjustment of satellite images based on a single image in sparse control conditions. The test results demonstrated the effectiveness and feasibility of the proposed method.

The ZiYuan-3 surveying satellite (ZY-3) is a high-precision civilian satellite imaging sensor. Since its launch on January 9, 2012, it has been in operation for one and a half years. Although the initial postlaunch ZY-3 geometric accuracy was verified during an in-orbit operation period, on-orbit calibration was still necessary from time to time. This on-orbit calibration has vastly improved the location accuracy in planimetry for ZY-3 panchromatic images. This letter briefly describes the principle of on-orbit calibration and production processes of sensor-corrected products. Furthermore, block adjustment based on a rational function model test showed planimetric and vertical accuracy values of 10 m and 5 m, respectively, without ground control points (GCPs). The accuracy values improved to 3 m and 2 m, respectively, with a few GCPs. The statistics results are from ten different regions with independent checkpoints (ICPs). All accuracy values are the root-mean-square error of ICPs. Therefore, ZY-3 can be used for the generation of cartographic maps at the 1 : 50 000 scale and for revision and updates of 1 : 25 000 scale maps. Compared with other mainstream high-resolution satellite images of the same ground resolution, ZY-3's geometric accuracy is almost the same and sometimes even better.

Ziyuan1-02C (ZY1-02C) was launched on December 22, 2011, and it is the first civilian high-resolution remote sensing satellite in China. However, the limited precision of the onboard attitude measurement system causes many errors during attitude transfer by ZY1-02C. Thus, there are complex distortions in the images obtained by ZY1-02C, which restricts its application greatly. In this paper, we consider the feasibility of attitude error correction based on parallel observations with high-resolution cameras, and the method is described in detail. To validate the efficiency of the proposed method, several images and corresponding control data were collected from the Henan, Taihang Mountain, Neimeng, and Taiyuan areas in China. The experimental results indicate that seamless mosaic images without distortion can be obtained using our method. Furthermore, the positioning accuracy with a few ground control points (GCPs) was shown to be better than 1.5 pixels and equivalent to the accuracy of the GCPs.

The ZiYuan-3 (ZY-3) remote sensing satellite is China's first civilian high-resolution stereo mapping satellite. Because the interior orientation parameters measured before launch are biased, the multispectral (four-band) images collected by ZY-3 exhibit low-accuracy band-to-band registration, which affects their subsequent applications. This paper presents a valid method for interior orientation determination of the ZY-3 multispectral sensor by determining the look angles of the charge-coupled device arrays for all bands. One band is chosen as the benchmark band, and its interior orientation is determined using the relevant ZY-3 image collected over the calibration field and the corresponding digital orthoimage map and digital elevation model. The remaining bands are then calibrated using the benchmark band as control data. The quality of the calibration is further enhanced by shortening the calibration period and by combining images collected over different calibration fields, which decreases the negative effects of errors in the satellite's attitude and position data. The interior orientation of the multispectral sensor in ZY-3 was determined using data sets taken over two calibration fields, namely, Dengfeng (Henan Province) and Tianjin. Evaluation experiments were performed using ZY-3 multispectral images and ground control points (GCPs) collected over several different periods and areas. The positioning accuracy of the ZY-3 multispectral images with a limited number of GCPs after calibration of the interior orientation was better than 0.3 pixels, and the band-to-band registration accuracy was up to 0.15 pixels.

InSAR is a technique to extract three-dimensional information on the terrain topography from the phase of the SAR signal and the rational polynomial coefficient (RPC) model has raised considerable interest in the photogrammetry and remote sensing communities. Some work has been done on the RPC model's substitutability for the SAR rigorous sensor model, which is based on the Range-Doppler (RD) equation, moreover, there have been study discussing the substitutability of the RPC model to InSAR (Interferometric SAR) rigorous geometrical sensor model, which is based on the RD equation and phase equation.For the InSAR process, baseline evaluation is very important for the final extracted DEM, but when using RPC model for the replacement of InSAR rigorous geometrical sensor model, the parameter of baseline cannot be included explicitly. Hence, we contend that phase evaluation can be substituted for the baseline evaluation, and it can be applied for RPC model, which can compensate the error caused by orbit and can rebuild the real phase.

The inner calibration technology of spaceborne SAR, as an effective way of eliminating the error deduced from inaccurate SAR positioning parameters to improve positioning accuracy, has been studied worldwide. Based on the imaging characteristics of spaceborne SAR and its rigorous sensor model, this paper constructs the rigorous geometric model for SAR inner calibration, and then proposes the inner calibrating algorithm. CRS1 images over Inner Mongol are used as test data. The positioning accuracy of the model is improved a lot after inner calibration which verifies the feasibility and accuracy of the algorithm.

This paper presents a general method to generate the linear push-broom cameras and SAR epipolar images, which is based on RPC model and image-space adjustment model for relative orientation. The epipolar curves are derived by endpoints growing and fitted with straight lines and hyperbolas. And the RPC model is used to replace the epipolar geometry model and the sensor model of original images. Ikonos along-track stereo, TerraSAR-X same side looking stereo and the mixture stereo of TerraSAR-X SLC image and SPOT 5 THX are tested to evaluate this method. The remaining vertical parallax of the Ikonos, TerraSAR-X and mixture stereos is 0.0040 pixels, 0.004 lpixel and 0.56 pixels, respectively.

Since the adoption of the rational polynomial coefficient (RPC) adjustment model as a preferred sensor orientation model for high-resolution optical satellite imagery, it has been demonstrated to be effective and robust. However, no publication discusses the application of the RPC adjustment model to the 3-D intersection from SAR stereoscopic pairs. This paper aims to validate the RPC adjustment model for spaceborne SAR stereoscopic orientation. Initially, a brief summary of the mathematical background of the RPC model is presented. Then, the SAR orientation errors are analyzed, namely, the orientation parameters, having the same net effect on the object-image relationship, and combined into a single adjustment parameter. The required adjustment is then discussed, and the formulation of the adjustment model is outlined. Finally, a number of designed adjustment experiments controlled via well-surveyed corner reflectors and an existing digital elevation model plus a digital orthophotograph map at the scale of 1:10 000 are performed. Multisensor images of TerraSAR-X, COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed), and Satellite Pour l'Observation de la Terre-5 (SPOT-5) over the Guangzhou area are used as test data. The results demonstrate that the proposed method can be generally applied to different imaging systems or the stereoscopic fusion of combined data and can achieve high orientation accuracy.