Jitter is a critical factor affecting the geometric accuracy of Earth observation satellites. After a satellite is launched, it is essential to detect and mitigate platform jitter to minimize its impact. The Ziyuan-3 (ZY3) series, China’s first civilian stereo mapping satellite constellation, meets the 1:50000 scale mapping accuracy requirement without ground control points. Detecting and suppressing jitter during the satellite’s orbit is a key technology for ensuring mapping accuracy. This letter investigates the causes of platform jitter across three satellites in the ZY3 series by analyzing gyroscope data using fast Fourier transform (FFT) at different stages of the satellite lifecycle. It compares the frequency and amplitude of jitter across different platforms and time periods. The results show that ZY3-01 and ZY3-02 exhibit consistent jitter patterns. They have significant amplitude in the 0.6 Hz region early in orbit and less later on, correlating with the satellites’ lifespans. In contrast, the 0.2 Hz region remains relatively stable. The ZY3-03 satellite, equipped with a highly stable platform, shows no significant jitter, evidencing the effectiveness of such platforms in jitter suppression.

LuTan-1 (LT-1) is the first civil L-band synthetic aperture radar (SAR) satellite constellation and comprises two identical satellites. LT-1 is designed to fulfill two main requirements, one of which the main tasks is to provide digital surface model (DSM) products covering the areas that are not available using optical satellites. The second task is to provide deformation products to support the geohazard monitoring task. For the first task, LT-1 provides a novel noninterrupted imaging technology to facilitate phase synchronization. For the second task, the orbit maintenance is implemented using a newly proposed in-plane offset semimajor axis and out-of-plane control triggered strategy. Besides, we provide the system performance analysis for the two main tasks. Finally, the first results are produced with root-mean-square-error (RMSE) of the DSM less than 0.7 m in the flat region and 6.7 m in the mountainous region. The RMSE of the first deformation products is less than 2.7 mm in the test region. The results indicate a favorable potential for terrain mapping and deformation monitoring applications.

LuTan-1(LT-1) has finished its in-orbit test in 2023. The inorbit test results show that LT-1 performed well and fulfilled the needs of the users. In order to use the data operationally for natural resources monitoring, especially for geohazard monitoring, we have established the application system for LT-1. In this paper, we have introduced the application system including the geometric technology, the standard products generation method, the basic deformation products generation method. Demonstrations of LT-1 are also provided to support the applications of the satellite data.

A collapse accident occurred in the Alxa left Banner open-pit mining area of Inner Mongolia on February 22, 2023, causing heavy casualties and property losses. Multi-temporal pre-collapse and post-collapse images were acquired by the Lutan-1 SAR satellites, which is the first group L-band SAR constellation in China. The subsidence information of pre-collapse and post-collapse were extracted and analyzed, which provides certain knowledge for post-disaster accident handling and emergency response. Due to the extensive mining activities, the topography of open-pit coal mine area generally changes obviously. TanDEM and Gaofen-7 DEM were employed for deformation monitoring. The results indicated that time-efficient DEM is necessary for open-pit mining area monitoring. At the same time, based on the subsidence information detected before the disaster with high spatial and temporal resolutions, combining with prior information and expert interpretation, it is potential to provide certain support for the early hidden danger identification.

A full-link simulation of satellite single-photon LiDAR systems is important in laser parameter design, laser performance, and index analysis. Therefore, in this letter, a full-link simulation method for satellite single-photon LiDAR systems is proposed to simulate the whole process of satellite single-photon laser emission, transmission, and the generation of signal and noise photon events. The proposed method initially employs the LiDAR equation to simulate the energy and photon count of single-photon laser echoes, and it rapidly generates photon events using the Monte Carlo method. With the Advanced Topographic Laser Altimeter System (ATLAS) as the experimental object, echo photon events for three ideal terrains and complex natural surfaces are simulated. The simulated photon point cloud of the ideal terrain is consistent with the real terrain. Additionally, the signal photons observed by ATLAS (ATL03 data) are used to validate the accuracy of the simulated photon point cloud. The results indicate that the root mean square error (RMSE) of the distance between the ATL03 photons and the simulated 3-D photon point clouds is approximately 2.0 m, and the Chamfer distance (CD) is approximately 1.5 m. The coefficient of determination between the simulated photon elevation and ATL03 photon is 1.0. These results provide substantial evidence of the high similarity between the simulated and actual photon point clouds.

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 L-band differential interferometric SAR satellite, also named as LuTan-1, is the first bistatic spaceborne L-band SAR constellation for multiple applications in China. The Datong, Yulin and Liupanshui coalfields are the main coal production base in China which produce over 100 million tons coal each year and play a significant role for the development of national economy. However, a series of issues such as ground subsidence, landslides and damage of structures are induced by extensive coal mining. Therefore, the preliminary assessment of LuTan-1 SAR data for mining deformation monitoring, including Datong, Yulin and Liupanshui coalmines in China, were implemented in this research. Owing to the high revisit ability and long wavelength of LuTan-1 satellites, obvious subsidence was detected for different coalmines using LuTan-1 SAR data. In comparison with the simultaneous leveling measurements, deformation monitoring with cm accuracy were achieved over the study area.

The GaoFen-7 (GF-7) satellite, which is used for Earth observations, is equipped with China’s first civil full-waveform laser altimeter. The geometric calibration of the GF-7 satellite laser is vital for ensuring its long-term stability and highly precise global measurements. In this study, a geometric of calibrating satellite laser is proposed. Notably, this method does not require an outdoor calibration field but instead relies on a mathematical surface model. This calibration method converts that to calibrate laser pointing angle error in traditional calibration method to calibrate the three-axis rotation angle error between the laser frame and the satellite body fixed-frame. Assuming that the laser footprints always fall on a fit spatial plane, a new satellite laser geometry calibration model is established. In addition, the GF-7 satellite laser is calibrated by using airborne light detection and ranging (LiDAR) point clouds collected from the sloped terrain of Xinjiang. Taking the laser footprints that are captured by the laser ground detectors as the true values, the positioning error of GF-7 beam 1 is improved from 430 m before calibration to 1.8 m after calibration. And the beam 2 positioning error is improved from 1025 to 6.4 m. Finally, according to the surface elevation of the laser footprints that were measured by a real-time kinematic, the elevation error of the GF-7 laser on flat terrain was verified to be 0.14 m after calibration. In summary, the calibration method proposed in this study is effective and feasible and provides a new method for satellite laser calibration.

Gaofen7 (GF-7) is the world’s first Earth observation satellite to integrate optical stereo cameras and laser altimeters, mainly serving global 1:10,000 scale stereo mapping. GF-7 can synchronously acquire laser altimetry points (LAPs) and stereo images with sub-meter resolution, aiming to effectively improve the vertical positioning accuracy of stereo images through joint geopositioning of LAPs and stereo images. This study proposed a quantitative evaluation model for theoretical vertical accuracy of GF-7 stereo images, LAPs, and the joint geopositioning of LAPs and stereo images. Without using ground control points (GCPs), the estimated theoretical vertical root mean square errors (RMSEs) of GF-7 stereo images, LAPs, and joint geopositioning of LAPs and stereo images were 2.02-8.49, 0.09, and 0.59-0.75 m, respectively. Subsequently, accuracy verification experiments were conducted using GF-7 data covering Hubei Province, China. The actual vertical RMSE of stereo images and joint geopositioning of LAPs and stereo images were 3.19 and 0.68 m, respectively, and the actual vertical RMSE of LAPs was consistent with the theoretical value. The experimental results confirm that the method proposed in this study is effective. The results of theoretical estimation and experimental verification both indicate that with the assistance of LAPs, the vertical accuracy of GF-7 stereo images without GCPs can meet the requirements of 1:10,000 scale mapping. The results of this study have important reference value for improving GF-7 data processing methods and guiding the design of future satellites that integrate optical stereo cameras and laser altimeters.

Lutan-l (LT-1) is the first Chinese SAR satellite constellation that provide continuous deformation images covering the whole China. Three deformation products are designed to conduct the geohazard monitoring tasks. The first is DInSAR deformation field product which can be used for general geohazards investigation. The second is stacking deformation velocity field that can be used for geohazards screening. The third is deformation time-series obtained from multi-temporal InSAR and is specially designed for the continuous deformation regions to discover the deformation rules in temporal domain. In this paper, the LT-1 key features for deformation monitoring are provided. Product examples are shown with the Setninel-1A SAR images covering Datong City, Shanxi province. The three deformation products are expected to be used for geohazard monitoring in China.

2-D phase unwrapping (PU) is one of the key steps in the processing of interferometric synthetic aperture radar (InSAR) data. PU in large-gradient areas has always been a fundamental problem. The phase continuity assumption makes it difficult to obtain ideal PU results in large-gradient areas. To address this issue, in this letter, we propose a phase slicing 2-D PU method using the L1-norm that can effectively reduce the phase gradient interval. First, the study area is analyzed using an external digital elevation model (DEM) and interferometric phase information to obtain the number of phase slices and assisted phases. Then, we cut the interferometric phase to obtain the sliced interferometric phase and use the single-baseline (SB) L1-norm PU method to unwrap the sliced interferometric phase. Finally, the unwrapped phase is recovered by summing the sliced unwrapped phases and assisted phases. Experiments are conducted with simulated and TanDEM-X InSAR data sets. Compared with the representative SB-PU method, the proposed method can effectively avoid the problem of large gradients in PU.

Improving the accuracy of block adjustment with few or no ground control points (GCPs) is one of the core issues for satellite imagery high-precision mapping. The GaoFen-7 (GF-7) satellite laser altimeter system is the first Chinese formal spaceborne laser altimeter system, which is equipped with laser footprint cameras for the first time. Given the very high height accuracy and high planimetric accuracy of the instrument, this letter proposed an adjustment method of ZiYuan-3 (ZY-3) satellite imagery by integrating GF-7 laser altimetry data. The laser control points (LCPs) were automatically extracted according to the registration of the footprint images and stereo images. Furthermore, the combined adjustment was performed with the LCPs as the vertical control and horizontal constraint. The performance of the proposed method was evaluated by using 417 stereo scenes of ZY-3 images and 954 LCPs, covering an area of 158 000 km2in Shandong, China. The results demonstrated that, only by using all LCPs as the vertical control, the root mean square error (RMSE) of elevation can be significantly improved from the original 8.82 to 1.40 m. Moreover, the planimetric RMSE decreased from the original 15.31 to 3.58 m with residual randomness when 45 LCPs were used as the horizontal constraint. Our method provides an alternative solution for satellite imagery geopositioning improvement without GCPs.

Laser footprint images (LFIs) are auxiliary sensors of satellite laser altimeters, which are mainly used to determine whether the laser pulse is obscured by clouds, and establish a link between the laser and the high-resolution image, which requires an algorithm for achieving high recognition accuracy while preserving the edge contour features of clouds. This study uses the AdaBoost algorithm to achieve the fusion of multiple semantic segmentation models and combines the dark channel priori model to enhance the overall cloud detection effect, realize the removal of thin clouds, and improve the matching accuracy of footprint and altimeter images. The experimental results show that the model outperforms the basic cloud detection model by approximately 14.89% and that the contours of the extracted cloud-covered area are more consistent with subjective human perceptions. Furthermore, the matching accuracy of the optimized footprint and high-resolution altimeter images improved by approximately 17.67%.

The GaoFen-7 (GF-7) satellite is the China’s first civil optical stereo-mapping satellite with submeter resolution. GF-7 is also equipped with a laser altimeter, which can obtain sparse laser altimetry points (LAPs) to improve the vertical accuracy of stereo images. Verifying the geometric accuracy of GF-7 imagery without ground-control points (GCPs) is of great significance for evaluating the mapping capability of GF-7. In this study, 147 stereo images and 804 LAPs of the GF-7 satellite covering Jiangsu Province, Hebei Province, and Sichuan Province in China were selected as experimental data. Subsequently, 455 high-precision independent check points were used to verify the geometric accuracy of the GF-7 images. The results show that, without GCPs, the root-mean-square errors (RMSEs) in the horizontal direction of the experimental images in flat, hilly, mountainous, and high-mountainous terrains, and over the entire area were 4.91, 3.81, 4.38, 6.39, and 4.91 m, respectively. The RMSEs in the vertical direction were 3.42, 4.68, 3.00, 4.41, and 3.52 m, respectively. After performing combined block adjustments of the LAPs and stereo images, the RMSEs of the experimental images in the vertical direction decreased to 0.43, 0.66, 0.76, 1.14, and 0.68 m, respectively, whereas those in the horizontal direction saw only minor changes. Therefore, in the absence of GCPs, the geometric accuracy of GF-7 imagery can fully meet the accuracy requirements of China’s 1:10 000 scale stereo mapping by using the GF-7 LAPs as control points.

The Ziyuan3-03 (ZY3-03) satellite is an optical stereo mapping satellite that is able to synchronously provide triple linear-array stereo images and discrete laser altimetry points (LAPs) for plotting 1:50 000-scale maps. Considering that the relative horizontal error between the ZY3-03 nadir image and synchronously acquired LAPs is very small, this study designed a technical method to obtain accurate image coordinates of LAPs on stereo images, and this method successfully overcomes the shortcomings of previous methods. Subsequently, a combined block adjustment model of stereo images and LAPs was constructed. On this basis, this study proposed a combined block adjustment method using ZY3-03 stereo images and LAPs to improve the vertical accuracy of images. For our research, 19 ZY3-03 triple linear-array stereo images and 87 synchronously acquired LAPs of plain, hilly, and mountainous terrains located in China were selected to conduct the block adjustment experiment. Results indicated that the vertical root-mean-square error of the experimental images decreased from 5.27 to 2.58 m for plain and hilly terrains and decreased from 11.25 to 4.45 m for mountainous terrain. Moreover, the vertical accuracy of the experimental images met the requirement of 1:50 000-scale mapping and also exceeded the accuracy of combined adjustment with Ice, Cloud, and land Elevation Satellite (ICESat)-1 satellite LAPs and the same experimental images.

Very high resolution (VHR) remote sensing images contain various multi-scale objects, such as large-scale buildings and small-scale cars. However, these multi-scale objects cannot be considered simultaneously in the widely used backbones with large downsampling factor (e.g. VGG-like and ResNet-like), resulting in the appearance of various context aggregation approaches such as fusing low-level features and attention-based modules. To alleviate this problem caused by backbones with large downsampling factor, we propose a feature-selection high-resolution network (FSHRNet) based on an observation: if the features maintain high resolution throughout the network, a high precision segmentation result can be obtained by only using a 1×1 convolution layer with no need for complex context aggregation modules. Specifically, the backbone of FSHRNet is a multi-branch structure similar to HRNet where the high-resolution branch is the principal line. Then, a lightweight dynamic weight module, named feature-selection convolution layer (FSConv), is presented to fuse multi-resolution features, allowing adaptively feature selection based on the characteristic of objects. Finally, a specially designed 1×1 convolution layer derived from hypersphere embedding is used to produce the segmentation result. Experiments with other widely used methods show that the proposed FSHRNet obtains competitive performance on ISPRS Vaihingen dataset, ISPRS Potsdam dataset and iSAID dataset.

Abstract In November 2019, the GaoFen‐7(GF‐7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high‐precision long‐range three‐dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U‐Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF‐7 data processing and related research on footprint images.

An AIEM based soil moisture retrieval strategy for bare agricultural area was developed in this study. In order to solve the ill-posed problem for soil moisture retrieval, multi-sensor SAR observations were integrated to estimate soil moisture based on the AIEM simulation. Soil surface parameters were derived using the least square method. The influence of surface roughness for soil moisture estimation was removed. Based on the in situ measurement and multi-sensor SAR images acquired on October 2015 over agricultural area, quantitative evaluation of the developed method was implemented. The results indicate that accurate soil moisture was obtained over the study area. The root mean square error of the estimated result is less than 0.05 cm3/cm3.

In this paper, radiometric cross-calibration of Ziyuan-3 Satellite was conducted at the homogeneous field test site in August 2015, using GF1 satellite and Landsat-8. The test site was chosen at Dunhuang site, which has been used for vicarious calibration for Chinese Satellite since 1990s. The radiometric cross-calibration of ZY3, GF1 and Landsat-8 was performed by the simultaneously observation of the three satellites on August 15, 2015. In this study, three calibration coefficients from various methods were compared and analyzed. This paper reveals that the cross-calibration of the ZY3 satellite using GF1 PMS sensor is better than using GF1 WFV sensor. The best result is the cross-calibration coefficient using Landsat-8 OLI, which is consistent with the calibration coefficient of the reflectance-based vicarious calibration method in Dunhuang. The validation results show the accuracy of the cross-calibration for ZY3 sensor using GF1 PMS and Landsat-8 OLI is less than 4% and 2%,respectively. This study can supply reference to the radiometric cross-calibration of Chinese satellite senor, and the results suggest that the radiometric cross-calibration coefficients can be useful for maintaining the quantitative application.

The automatic extraction of small objects such as roadside milestones, small traffic signs, and other urban furniture remains a technical challenge. This study focuses on methods of deep learning to detect small urban elements in mobile mapping system (MMS) images. Based on images obtained by an MMS in urban areas, we create an urban element detection (UED) data set containing several kinds of small objects found in a city. A simple feature extraction convolution neural network (CNN) called SlimNet is proposed and combined with an optimized faster R-CNN framework. The resulting deep learning method can automatically extract small objects commonly found in cities, including manhole covers, milestones, and license plates. Experiments on the UED data set show that SlimNet has the highest accuracy compared with other popular networks, including VGG, MobileNet, ResNet, and YOLOv3. The SlimNet model can achieve a mean average precision (AP) that is up to 12.3% higher than that of the lowest ResNet-152 network and can accelerate both training and detection owing to its relative simplicity. Moreover,$k$-means clustering is used to choose the dimensions of the anchor box for detection. We ran$k$-means clustering for different numbers of clusters, and the results show that at least four clusters are needed for detection using a small data set such as the UED. We also propose a method to use templates of different scales for anchors to further improve small object detection; this approach improved the AP by 3%–4% in our experiments.

Gaofen-3 (GF-3) is the only in-orbit civilian SAR satellite of China. The satellite is specially designed for ocean observation. The coherence is not exclusively considered during satellite designing phase. In this paper, we conduct the preliminary coherence assessment of GF-3 SAR data. Decoherence sources of GF-3 consists of seven components. Baseline decoherence, signal-to-noise decoherence as well as the volume decoherence affects the coherence severely. While the last four components, i.e., volume decoherence, ambiguity decoherence, Doppler spectral decoherence and processing decoherence contributes to the coherence values slightly. We select 13 pairs of GF-3 SAR data and analyze the coherence. Results of the selected data show that the coherence values after multi-looking, flattening, elevation phase removal and filtering exceed 0.4 even at the dessert regions, making it potential in InSAR related applications such as DEM generation and deformation monitoring.

We generated DSM of all China using over 37 thousand stereo and tri-stereo ZY3 images. The original RPCs were modified though bundle adjustment oriented by SRTM. DSMs were generated through dense tri-stereo matching based on a tri-epipolar method. Modified Belief Propagation was used as global method which was more robust than standard SGM. All original DSMs were multi degree overlapping. For every 0.0001 degree horizontal grid geographic coordinate, a middle height was calculated from all DSMs containing this coordinate. All other heights whose distance from this middle height bigger than a threshold were discarded and the rest heights were averaged as result. Over 330 thousand check points were used to estimate the height accuracy of all DSMs. The RMSE is about 4 meters.

This letter expands the block adjustment method by, respectively, adopting high-precision global positioning system (GPS) points, geoscience laser altimeter system (GLAS) data, and Shuttle Radar Topography Mission (SRTM)-digital elevation model (DEM) as vertical control data. Block adjustment experiments were conducted with these control data, with 388 ZY-3 satellite stereo image pairs across over 18600 km2of land in Hubei province of China utilized as experimental images and 180 GPS points as checkpoints. The experimental results obtained show that, with the adoption of 23 GPS points as control points, the horizontal root-mean-square error (RMSE) and vertical RMSE of the ZY-3 image improved from 10.97 to 5.72 m and from 7.12 to 1.65 m, respectively. Furthermore, with the adoption of 326 GLAS data points as vertical control points, the vertical RMSE of the ZY-3 images of the flat terrain areas and mountain terrain areas improved to 1.69 and 3.62 m, respectively. Through block adjustment constrained by SRTM-DEM, the vertical RMSE of the ZY-3 images was 1.44 m for flat terrain areas and 3.05 m for mountain terrain areas, respectively. The vertical accuracy of the satellite images was significantly enhanced, especially on the flat terrain area, when GLAS and STRM-DEM were used for vertical control block adjustment, which is similar to the vertical accuracy of block adjustments adopting GPS points as control.

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.

Platform oscillation is a crucial error source that undermines the geometric performance of satellite imagery. In this paper, an approach for oscillation detection that utilizes the parallax observation between multispectral bands is proposed. Due to the parallax observation configuration of the multispectral sensors, the attitude oscillation of the ZY-3 satellite can be detected and estimated by the parallax disparities between adjacent band images. The parallax disparities between bands are obtained through a high-accuracy image matching method based on phase correlation at the subpixel level. The pixel displacements caused by the satellite oscillation are then retrieved from the parallax disparities by the use of two proposed transformation models. Experiments using both single-scene and long-strip images were conducted in order to retrieve the frequencies and amplitudes of the oscillation, as well as its changing trend. The experimental results for the ZY-3 satellite demonstrate the following findings: 1) the oscillation components obtained contain a distinct frequency of around 0.65 Hz; 2) the amplitude of the oscillation displacement on the image plane ranges from 0.5 to 1.5 pixels in the cross-track direction and from 0.2 to 0.6 pixels in the along-track direction, respectively; and 3) the oscillation frequency detected from the images is in agreement with that from the original attitude data.

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.