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.

While systematic radiometric errors can be mitigated through on-orbit calibration, correcting low-frequency, time-varying errors often induced by complex imaging dynamics remains a greater challenge. This study introduces a correction method tailored for optical remote sensing platforms to overcome such errors in JiTian-03 imagery, particularly those arising from high-agility curve imaging. The process begins with relative radiometric calibration to correct high-frequency inconsistencies among sensor detectors, thereby suppressing striping and banding noise in the imagery. Next, the image is decomposed into low-frequency background and high-frequency detail components. A column-wise moment-matching algorithm is then applied to the low-frequency background, aligning each column’s first- and second-order central moments with global statistics to derive correction coefficients. This approach effectively compensates for low-frequency radiometric errors while preserving high-frequency texture details in the image. Experimental results confirm that, following correction of both high- and low-frequency radiometric errors, JT-03 imagery exhibits a substantial reduction in striping, banding, and brightness nonuniformity. The correction yields a uniform radiometric response across the entire field of view while preserving ground texture fidelity and image detail. As a result, the radiometric uniformity of JT-03 image products is significantly enhanced. Quantitatively, post high-frequency calibration, the striping index across all JT-03 bands improved to within 0.1% and the standard deviation of column means to 4.7%. Subsequent low-frequency correction reduced the standard deviation of column means by an average of 73.89%, achieving a final value of better than 1.3%.

Accurate cloud detection in remote sensing images constitutes a critical preprocessing requirement for ensuring the validity of subsequent analytical applications. Existing cloud detection methods effectively identify primary cloud regions but struggle with precise boundary delineation and distinguishing spectrally similar surfaces, particularly between clouds and terrain with comparable reflectance properties. To address these challenges, we propose the coarse-to-fine progressive network (CFPNet), a progressive detection framework integrating across channel and spatial dimensions. The framework incorporates two innovative components: a channel spatial attention residual fusion module (CSARF) and a multi-mask adaptive attention module (MMAA). The CSARF module achieves the network’s initial focus on clouds, while the MMAA module enables fine feature extraction of clouds. Specifically, The CSARF module employs channel attention (CAM) and spatial attention (SAM) mechanisms to suppress background noise while enhancing discriminative feature representation. MMAA employs muti-mask self-attention (MMSA) to compute channel self-attention and capture long-range dependencies, while using a multi-mask strategy to filter important channels. Deformable contextual feed-forward network (DCFN) then adaptively extracts cloud boundary features through deformable convolution, minimizing non-cloud pixel interference. Hence, the network enables coarse-to-fine feature extraction of clouds across both channel and spatial dimensions. Experimental results on the GF1-WFV, AIR-CD and Sentinel-2 datasets demonstrate that our method achieves superior performance in boundary detail accuracy and inter-class feature classification compared to other methods.

Deep learning-based disparity estimation methods have demonstrated significant potential in optical satellite stereo image applications. However, learning-based methods remain susceptible to domain shifts caused by spatiotemporal variations and stereo-sensor heterogeneity. To address these challenges, we propose a Hierarchical Domain Adaptation Disparity Estimation framework (HDADE) for optical satellite stereo images. HDADE was structured with a four-stage technique pipeline to improve the training data quality and diversity, explicitly align the spectral and stereo distribution, implicitly enhance the robustness of feature extraction and matching, directly facilitate feature alignment with the target domain. This hierarchical framework systematically mitigates disparity estimation accuracy degradation in cross-domain scenarios. Cross-spatiotemporal and cross-payload generalization experiments were conducted based on the WHU_Stereo and US3D datasets. The experimental results show that HDADE significantly outperformed other advanced methods and possessed plug-and-play versatility. Notably, greater domain shift scene transfer experiments indicated that, with limited annotation data, HDADE has the potential for large-scale automatic applications.

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).

Building height is one of the important data for understanding urban development and changes. Building height estimation using a digital surface model (DSM) based on the difference between the roof elevation and the ground elevation of the building is commonly utilized. However, owing to the limitations of existing DSM techniques, invalid values may exist in the DSM. Existing DSM-based methods for estimating building heights typically use the interpolated DSM; however, when there are many invalid values, there may be errors in the interpolation results, which can mislead the selection of ground elevation values. Therefore, we propose a building-height extraction method that combines an optimal selection region and a multiindex evaluation mechanism to reduce the impact of invalid values and complex terrains. First, the optimal area for the ground elevation search was obtained based on the spatial relationship between the target building and surrounding buildings. Second, a joint multiindicator weighted evaluation mechanism was used to obtain the optimal ground elevation value. Finally, the building height was determined based on the difference between the roof and the ground elevations. Four build-up areas were used to test the effectiveness of the proposed method. The results exhibit high accuracy in complex areas with variable ground elevations, with a mean absolute error (MAE) of 1.16 m in building height. In areas with many invalid values and large shadow coverage of the surface areas, the MAE in building height is 0.92 m. Additionally, we verified the accuracy of the ground elevation estimated after interpolation. It is evident that the performance of the original DSM is satisfactory, with a high tolerance for input data and ability to be used in different building scenarios, providing new ideas for studying building height estimates.

In the field of photogrammetry and remote sensing, the task of satellite stereo image disparity estimation (SSIDE) has long been recognized as both challenging and important. Currently, deep-learning methods are gaining prominence in the SSIDE domain. However, the inconsistency between stereo images and ground truth makes the fine training and accurate inference of SSIDE networks extremely difficult. Furthermore, the existence of textureless and repeated texture areas in satellite images complicates the execution of end-to-end SSIDE networks, especially in areas with variable illumination conditions. In this study, a sparse matching point-guided disparity estimation (SMP-DE) general framework was introduced to address such concerns. SMP-DE employed sparse matching point-guided data evaluation and distillation (SMP-DED) for fault-tolerant training and ensuring unbiased guidance training as well as reliable reasoning. In addition, SMP-DE executed optimization for the disparity estimation network across various feature spaces by integrating sparse matching point-guided feature contrastive registration (SMP-FCR) and matching cost uniqueness constraint (MCUC) modules. Therefore, SMP-DE can mine homogenous features and model low-entropy matching costs in challenging regions. Experimental results demonstrated that SMP-DE has outstanding disparity estimation accuracy and generalization compared with other advanced methods. Furthermore, the proposed SMP-DED exhibited excellent flexibility and generality, since it can be combined with various disparity estimating networks, giving the networks an accuracy boost on a range of datasets. In summary, SMP-DE provides a novel perspective for end-to-end SSIDE research.

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.

Landslides are common and highly threatening geological disasters, and detecting landslide occurrence areas using remote sensing images has important social significance for disaster warning, prediction, and proactive rescue. Currently, the most effective and direct application is in-orbit real-time detection, which obtains the boundary, location and other relevant information of the post-disaster areas by performing in-orbit calculations on the raw images, thereby shortening the relief cycle. However, challenges associated with this application are high efficiency and portability, while overcoming complex surface distributions to ensure high accuracy. In this article, we propose a lightweight network framework and deploy it on a civilian QK series environmental monitoring satellite, transmitting the detection results in graphic form to the ground station. This framework addresses the problems of parameter redundancy and low real-time performance in existing embedded device landslide detection methods. The model adopts a conditional attention mechanism, multifeature fusion, and asymmetric upsampling modules (AUMs). It employs only 3.045 MB of parameters, with a mean intersection over union (MIoU) of 88.38% and an F1-scores of 93.77%, striking a good balance between computational efficiency and accuracy, and its performance can meet the requirements of in-orbit applications. Additionally, we have labeled and published landslide samples that occurred in Luding, Sichuan Province, Southwest China, in September 2022, which is available athttps://github.com/fuadou/project/tree/main/Luding_datasets.

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.

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.

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.

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.

As the geometric resolution requirements of hyperspectral imaging systems become higher and higher, insufficient energy becomes more of a problem. Motion compensation using "delayed imaging" is a common design method but also brings out some imaging quality and ground-processing problems. This paper described the system design, onboard measuring device and processing techniques of motion compensation for the high-resolution hyperspectral imaging satellite.

We discuss the Optical Image Matching Technique, which cloud obtains the horizontal ground deformation by using the frequency domain phase correlation technique and Fourier migration theory. Then, we retrieved the fault rupture of the 2014 Mw6.9 Yutian earthquake from GF-1 and ZY-3 images, both of which are Chinese optical satellites. The experiment indicates the ability that Chinese optical satellites cloud monitors the seismic deformation.

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.

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.