Plenty of effort has been devoted to solving the nonlinear radiation distortions (NRDs) in planetary image matching. The mainstream solutions convert multimodal images into “single” modal images, which requires building the intermediate modalities of images. Phase congruency (PC) features have been widely used to construct intermediate modalities due to their excellent structure extraction capabilities and have proven their effectiveness on Earth remote sensing images. However, when dealing with large-scale planetary remote sensing images (PRSIs), traditional PC features constructed based on the log-Gabor filter take considerable time, counterproductive to global topographic mapping. To address the efficiency issue, this work proposes a fast planetary image-matching method based on efficient PC-based feature transform (EPCFT). Specifically, we introduce a method to calculate PC using Gaussian first- and second-order derivatives, called efficient PC (EPC). Different from the log-Gabor filter, which is sensitive to structures in a single direction,$\rm EPC$uses circularly symmetric filters to equally process changes in all directions. The experiments with 100 image pairs show that compared with other methods, the efficiency of our method is nearly doubled without loss of accuracy.

Accurate lunar rock detection is vital for lunar exploration. However, the existing methods are sensitive to factors, such as the uneven lighting and terrain relief. To address these issues, a novel method combining multiscale phase feature type maps (PFTMs) and phase congruency moment maps (PCMMs) is proposed. First, rock seeds are detected through phase congruency and gradient analysis. Second, a strategy called the local scale saliency score (LSSS) is proposed to adaptively estimate the optimal scale layer for candidate rock detection. Within this layer, the specifically designed local-contextual and global-contextual (LGC) features are employed to identify the regions of interests (ROIs) for rocks. Subsequently, the process of filtering false positives (FPs) involves the utilization of geometric metrics and scale feature analysis. Finally, a specially designed edge detector named the bilateral local maximum PCMM-PFTM path is proposed to describe the edges of the rocks. Tests on Chang’E-3 and Chang’E-5 Landing Camera (LCAM) images show the proposed method’s robustness in detecting lunar rocks of varying sizes and reflectance, achieving F1-scores ranging from 0.919 to 0.947.

In planetary environments, conducting autonomous exploration tasks requires rovers to autonomously navigate the scene and achieve a detailed understanding of the terrain. Vision-based simultaneous localization and mapping (SLAM), which utilizes compact and low-power visual sensors for autonomous exploration, offers significant advantages in hardware deployment. While several methods have been proposed to apply visual navigation in planetary scenarios, they often rely on aerial imagery from orbiters and high-resolution DEMs for assistance. Additionally, accurate camera poses are typically required for dense matching during scene reconstruction, and the inability to perform loop closure significantly limits the performance of visual SLAM. Here, we propose a monocular visual SLAM approach combined with an adjusted neural radiance field for autonomous navigation and 3D reconstruction in planetary environments. Our approach solely relies on visual images as input and leverages the powerful learning capabilities of neural radiance fields to adapt to unseen scenes while simultaneously regressing both camera poses and scene representations. The estimated depth maps and poses can be further used for 3D reconstruction, assisting planetary exploration missions. The proposed method was tested on the Devon and MADMAX datasets that simulate planetary environments and achieved remarkable results. Even under the fixed rover navigation perspective, our pose estimation accuracy outperforms classical visual SLAM and other deep learning-based SLAM methods. Additionally, our novel view synthesis results exhibit quality comparable to those in terrestrial scenes. Comparisons with MVS techniques in terms of 3D reconstruction demonstrate that our approach recovers finer surface details. We also applied our method to the Perseverance rover dataset and achieved satisfactory positioning and reconstruction results in a real Martian environment, proving the practical feasibility of our method.

Digital Surface Models (DSMs) extracted from multi-view satellite images have extensive applications in the filed of photogrammetry. Although Neural Radiance Fields (NeRF) has shown significant potential in 3D reconstruction, most existing NeRF methods for satellite scenes adopt an end-to-end single-branch network structure, making it difficult to achieve fine-grained modeling of multiple complex terrain simultaneously, and performs poorly in weak-texture regions. Meanwhile, deep MLP structures are prone to information degradation during feature transmission, further affecting the completeness and accuracy of DSMs. To address these challenges, we propose RDS-NeRF, a novel NeRF framework integrating residual feature enhancement and depth supervision. The method introduces a residual feature enhancement structure to alleviate the problem of information degradation during feature transmission in the network and improve the model’s ability to model local details and low-texture regions. Additionally, estimated depth maps are incorporated as global geometric priors to guide the network in constructing more accurate and complete 3D structures. Experiments on the WorldView-3 satellite imagery datasets across multiple typical land cover types (building, road, water body, and vegetation) and complex scenes integrating multiple land features demonstrate that RDS-NeRF outperforms mainstream methods in terms of DSM accuracy, completeness, and novel view synthesis quality. Ablation experiments further validate the complementarity and effectiveness of the residual enhancement and depth supervision mechanisms across different scene types. In conclusion, RDS-NeRF provides a new and effective solution for generating high-quality DSMs from satellite imagery with adaptability to multiple scenes. Code will be available at https://github.com/dfsvdgf/RDS-NeRF.

Accurate simulation of subsurface physical temperatures in lunar south polar craters is essential for thermal environment analysis in landing site selection. In this study, we developed an improved method for simulating large-scale subsurface temperature by integrating multi-source datasets, including Lunar Reconnaissance Orbiter (LRO) DEM, LRO Diviner infrared brightness temperature (TB), and Chang’E-2 microwave TB. The proposed method accounts for differences in heat sources between non-permanently shaded regions (non-PSR) and permanently shaded regions (PSR) craters, applying effective solar irradiance with terrain effect for non-PSR craters, and calibrated infrared TB with emissivity effect for PSR craters. Additionally, we incorporated key model parameters, including an annual model period and updated thermal conductivity, into a one-dimensional (1-D) heat transfer model. This method was applied to simulate subsurface temperatures (0–2 m depth) in lunar south polar craters, validated by the microwave radiative transfer model and observed microwave TB data. The simulation results indicate that temperatures near the lunar south polar are generally lower than those in the 80°-85°S latitude range. These temperature profiles can be applied to quantitatively estimate the detection depths of heat flow and buried water ice. Furthermore, the model period considering seasonality shows a stronger impact on temperature simulations than other model parameters — altering temperatures by 20–40 K in non-PSR and by 20–30 K in PSR. Our method and findings provide valuable insights for future scientific exploration of the lunar south pole region and contribute to a better understanding of subsurface thermal evolution.

Digital elevation models (DEMs) play a crucial role in scientific research and exploration mission of the lunar south pole regions, as they offer vital topographic and morphological information of terrain surfaces. Surface points processed from various remote sensors provide a discrete but accurate measurements of lunar terrains. However, reconstructing high-resolution terrains from surface points poses significant challenges for neural networks primarily due to the lack of grid connectivity inherent in unordered surface points. Therefore, this study develops a point-based terrain reconstruction framework that incorporates learnable neural shape priors to better capture complex terrain geometry. To model structural characteristics inherent in three-dimensional (3-D) surface points, signed distance field (SDF) is introduced as a continuous representation of lunar surfaces. Moreover, we train a 3-D point-based encoder–decoder network that allows terrain modeling from unstructured surface points with encoded planar features. Additionally, a dynamic planar features strategy is proposed to optimize the framework’s performance to the characteristics of terrain flatness. Experiments were conducted on photoclinometry DEMs of the lunar polar region to demonstrate the advantages of our proposed framework. The qualitative and quantitative results highlight that our framework reconstructs accurate terrain geometry, outperforming traditional methods in terms of elevation, slope, aspect and roughness. Furthermore, the model exhibits a generalization ability when applied to both Lunar Orbiter Laser Altimeter (LOLA) measurements and unseen regions, underscoring its effectiveness in lunar exploration tasks.

The accurate matching of planetary remote sensing images (PRSIs) is the premise of accurate planetary terrain mapping. However, PRSIs often lack apparent man-made structures such as buildings or roads, leading to difficulties in feature description. In addition, the PRSIs collected by different sensors are affected by the imaging mechanism and the solar illumination, and there are obvious nonlinear radiation differences (NRDs). These problems make the matching of PRSIs difficult. To address the above issues, this article proposes a PRSI matching method based on optimized bandpass filtering and local topological prior, divided into two stages: coarse matching and fine matching. In the coarse matching stage, we first use the bandpass filtering to calculate the phase congruency (PC). Then, the feature block descriptors are constructed, and the local topology consensus is used to achieve the coarse alignment of feature blocks. Finally, we extract the point features and use the matching results of block features to narrow the matching range of point features. Based on the coarse matching results, the precision and reliability of the results are further improved through fine matching. The experimental results achieved with a PRSI dataset with 75 image pairs demonstrate that our method is superior to other recent methods, the matching accuracy of the proposed method is improved by more than 2.367 pixels, and the success rate is improved by over 22.667%. The source code will be publicly available athttps://github.com/WGY-RS/OFLP.

Mapping of planetary surface using stereo orbital imagery is a basic task of planetary exploration. As the number of orbiter images increases, excessive images will lead to unnecessary time costs and accuracy loss during the photogrammetric mapping process. However, existing studies focus more on the factors that affect the mapping accuracy and often ignores the redundancy and distribution rationality of images in the mapping process. Therefore, optimal data selection from extremely redundant orbiter images has become a challenging and urgent issue for planetary mapping. This paper proposes an effective optimal selection method of off-track stereo pairs, which extends the selection operation from a local to a global perspective, and reduces the redundancy of image pair subset covering the region of interest (ROI) based on multi-constraints such as quality constraints and global distribution constraints. Specifically, the ROI is rasterized and gridded, and the optimal and candidate stereo pairs are selected for each grid based on the quantified quality constraints. By combining global distribution constraints and consistency constraints between adjacent stereo pairs, an optimization model is constructed and the optimal subset of stereo pairs is determined using belief propagation algorithm. Experiments conducted on the selected ROIs of Moon and Mars indicate that our method significantly reduces data redundancy while ensuring the quality and rational distribution of stereo image pairs. Qualitative and quantitative evaluations demonstrate that the stereo pairs obtained using the proposed method perform exceptionally well in terms of intersection angles, redundancy rates, and other factors, showing overall superior performance compared to other methods. Additionally, the stereo pairs obtained by the proposed method were employed for photogrammetric mapping within the ROI, and the results validate the mapping products also exhibit satisfactory accuracy.

Deep learning-based feature matching methods have been actively explored for satellite image bundle block adjustment, leveraging their inherent robustness to large geometric distortions and radiometric differences—key challenges in multi-source optical image processing. However, their practical utility remains limited by two critical bottlenecks: poor adaptability to extreme scale variations and prohibitive computational costs for large-format satellite data. To address these limitations, we propose a learning-based feature representation method enhanced by nonlinear diffusion filtering, with two targeted innovations: (1) Nonlinear diffusion filtering with terrain-adaptive parameters is integrated into a novel image tiling strategy, which preserves local feature integrity while enabling consistent correspondence across heterogeneous satellite data sources; (2) A top-down pyramid construction mechanism that incorporates local continuity constraints and an adaptive matching strategy selection protocol optimizes scale space exploration efficiency while safeguarding matching quality. Experiments on Earth observation and Martian image datasets validate the method’s superiority: it achieves the highest matching success rate (97.25%) and the highest BBA accuracy(1.65 pixel) among competing approaches, alongside competitive efficiency. This performance advantage is particularly pronounced under extreme scale differences and challenging imaging conditions, confirming its suitability for high-precision remote sensing applications.

The 3D reconstruction of the lunar surface is a fundamental task, with stereo matching algorithms play a crucial role. The cornerstone of stereo matching is the computation of the matching cost, a step that significantly influences the ultimate result. In this paper, an improved census cost computation method is proposed and integrated in the Semi-Global Matching(SGM), which incorporates two-bit string to encode the relationship between pixels and ternary operator combined with an adaptive threshold. In this way, the matching robustness at depth discontinuities can be effectively improved and is particularly suitable for the case of multiple craters on the lunar surface. Experimental results show that the proposed method can greatly improve the matching results at lunar craters.

A rational function model (RFM) has long been a versatile tool in image block adjustment when building the global control network in lunar topographic mapping. By unifying the physical imaging model with mathematical expressions, the RFM facilitates using multisource images from various missions. However, the scarcity of ground control points (GCPs) on the lunar surface and the imaging void of Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) limits the application of RFM in large-scale mapping tasks. This work proposed a time-based RFM generation approach for strip-based image block adjustment using merely sparse control points. The proposed time-based RFM integrates separated image pieces in the same orbit into a cohesive whole for strip-based block adjustment, requiring merely sparse control points when mapping and geopositioning all the image pieces. Besides, we employ the elevation control information extracted from Lunar Orbit Laser Altimeter (LOLA) data in the strip-based block adjustment. Experiment results indicate that under the same sparse control conditions, the elevation accuracy using strip-based adjustment is improved by an average of 12.8% compared with single-based adjustment, and our method achieves a similar adjustment accuracy using sparse elevation controls as that in the densely controlled single-based block adjustment.

Matching multimodal remote sensing images (MRSIs) is a challenging task. Due to significant nonlinear radiation differences (NRDs), traditional image-matching methods cannot achieve satisfactory results. This article shows that structural information can get more robust matching results compared with texture information (i.e., gradient features) from images. In order to better explore the structural information of images, this article proposes an MRSI matching method using structure saliency features, called weighted structure saliency feature (WSSF). Two strategies are investigated and integrated into WSSF to improve the matching performance. The scale space is constructed based on the pointwise shape-adaptive texture scale filtering, which can better retain the structure features, and the second-order Gaussian steerable filtering, edge confidence map, and phase features are combined to establish the structural saliency map combined with second-order Gaussian steerable filtering, which is much more robust to NRD than traditional gradient map. The performance of the proposed method was evaluated on a total of 120 image pairs from two MRSI datasets and compared with the state-of-the-art matching methods, including the histogram of the orientation of weighted phase (HOWP), locally normalized image feature transform (LNIFT), co-occurrence filter space matching (CoFSM), radiation-variation insensitive feature transform (RIFT), local phase sharpness orientation (LPSO), and position-scale-orientation scale-invariant feature transform (SIFT) (PSO-SIFT). The experimental results indicate that WSSF obtains satisfactory and reliable results in terms of success rate (SR) and matching accuracy. Compared with the above six methods, the matching accuracy of WSSF is improved by more than 20.275%, and the SR is improved by over 5.833%. The source code will be publicly available athttps://github.com/WGY-RS/WSSF.

The investigation of small lunar craters holds scientific and engineering significance. This paper presents a novel method for detecting small lunar craters. It consists of three stages: seed detection, candidate crater detection, and crater evaluation. Firstly, crater seeds are identified through morphological operations as pixels with the highest local gradient and specific gradient direction. Secondly, the optimal scale for each seed is estimated based on the maximum response of the established phase congruency maximum moment (PCMM) scale space. For detecting very small craters, a crater detector called statistical morphological constraint path-sets (SMPS), which leverages image spatial domain features, is proposed. It configures the image as a weighted directed graph, using path-sets centered on seeds to flexibly detect highlights and shadow regions of craters. For detecting craters with larger optimal scale, another crater detector named structural consistency constrained multi-paths (SCMP) is proposed, utilizing the frequency phase features. The core idea of SCMP is to configure the phase feature type (PFT) map with the optimal scale as a directed graph. Centered on the seed, the multi-path operator is designed to detect craters. Unsupervised discriminative correlation filters (UDCFs) are trained with HOG features from images or PFT maps to validate candidate craters. The results indicate that for images with a resolution of 0.5-2m/pixel, the proposed method demonstrates good detection performance for small craters with diameters of less than 5 m, 5-10 m, and greater than 10 m, with an average detection rate of 0.89, 0.91, and 0.92, respectively.

The blur kernel estimated by a blind deblurring algorithm is hardly to be error-free. The blur kernel error is usually ignored in the nonblind deconvolution stage and may result in severe artifacts or other negative effects. In addition, the bias hidden in the blurry image formation model has not been found and investigated due to ignoring the existence of the blur kernel errors. To this end, we develop a nonblind deconvolution method by bias correction for inaccurate blur kernels in this article, which are constructed on the basis of the classic errors-in-variables (EIVs) model. First we analyze in detail the bias caused by errors of inaccurate blur kernel from blurry image formation model. Next, the latent sparsity property of jointed latent image and errors of inaccurate blur kernel is counted statistically, which is imposed as a new regularization term. Then, the objective function of the new nonblind method is established, and an alternative minimization algorithm is derived and employed to estimate the latent clear image. Furthermore, a filtering method is utilized to modify the bias term, which is added to amend intermediate image in each iteration. Finally, extensive experiments with benchmark datasets and blurred micro-nano satellite remote sensing images are carried out to evaluate the proposed method. Experimental results demonstrate that the proposed method can obtain high-quality restored images, and it is comparable to or even better than some state-of-the-art nonblind deconvolution methods.

Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds. However, the finding of reliable correspondence relies on establishing a robust and discriminative description of elements and the correct matching of corresponding elements. In this letter, we develop a coarse-to-fine registration strategy, which utilizes rotation-invariant features in frequency domain and a new graph matching (GM) method for iteratively searching correspondence. In the GM method, the similarity of both nodes and edges in the Euclidean and feature space is formulated to construct the optimization function. The proposed strategy is evaluated using two benchmark datasets and compared with several state-of-the-art methods. Regarding the experimental results, our proposed method can achieve a fine registration with rotation errors of less than 0.2° and translation errors of less than 0.1 m.

For time-delay integration (TDI) linear push-broom satellite images, it is generally considered that the matching window size and TDI stage number affect the jitter detection capability, but there are very few quantitative studies. In this letter, a scheme for investigating the effect of the matching window size and TDI stage number on image-based jitter detection is designed, and the model of image motion caused by jitter and the model of jitter detection and estimation for TDI images are deduced and established. Through a comprehensive experimental analysis, it is revealed that the influence of the TDI stage number and matching window size on jitter frequency detection is not significant, which improves our previous understanding. Although the TDI mode will attenuate the jitter amplitude by integration, the real jitter amplitude can be accurately retrieved by the proposed estimation model with attenuation compensation. The matching window size in the row direction has a significant effect on jitter amplitude detection. To ensure the detection accuracy of the jitter amplitude, the matching window size in the row direction is suggested to be less than 1/5 of the image line count corresponding to a jitter cycle.

As the accuracy of satellite laser altimetry is susceptible to real-time atmospheric conditions, with-in footprint topography fluctuation, and detector noise, etc., we proposed a method by comprehensively analyzing the laser ranging error and evaluation labels to extract high-accuracy elevation control points that is suitable for satellite imagery based topographic mapping applications. Using the ICESat laser altimetry data, a global high accuracy laser altimetry dataset including more than 60 million control points, based on the laser altimetry ranging error model and waveform quality analysis is proposed by the paper. For land areas, except for areas of water, snow/ice, and polar ice sheets, the dataset can provide the elevation control points for worldwide satellite topographic mapping using high spatial resolution imageries or other science researches that depend on accurate earth’s elevation information. We further used airborne lidar data from six study areas around the world to carefully validate the dataset’s accuracy. The results showed that, this dataset can meet the accuracy requirement of global mapping using high spatial resolution satellite imageries in terrains with a slope below 25°. Compared to the raw dataset, the proportion of footprint elevations that conform to the accuracy standard (0.5m@ slope<2°, 1.5m@ 2°≤slope<6° and 3m@ 6°≤slope<25°) is increased from 68.24%, 59.97% and 26.52% to 87.58%, 90.04% and 83.91% respectively. This method can assure that its extracted results’ accuracy is either very close to or better than that obtained by the methods proposed in relevant studies, with a much larger number of laser footprints have been reserved.

Array interferometric synthetic aperture radar (Array InSAR) has a 3-D resolution capability and solves the layover problem in interferometric SAR (InSAR) by arranging multiple antennas in the cross-orbit direction. Airborne Array InSAR point clouds are obtained from two scans for complete building information in urban areas, resulting in very low overlapping point cloud. The existing methods are difficult to extract the identical features for the registration of Array InSAR point clouds. To this end, a robust registration approach Array InSAR point clouds in urban areas is proposed in this study. The main contribution of this article is raising the theoretically optimal transformation for achieving point cloud registration, considering the constraint from parallel facades of a certain building. Point density estimation is adopted to retain building facade points for initial registration. The facade pairs of a specific building are then matched and divided into two categories by judging whether one contains the concave–convex features or not, for performing rotation rectification and fine shift fixation, respectively. Experimental results of both simulated and real data validate the feasibility and reliability of our approach. For the simulated data, the results reach an average rotation error of about 0.01° and an average translation error of less than 0.8 m. For the real data, two evaluation criteria are designed for the lack of reference data. The results reach an average of 0.4° of the defined angle difference and less 0.8-m distance difference from the source facades center to the normal extension of the target facades.

Image matching for blurred images is one of the most important and frequently discussed topics. In order to improve the precision of image matching for blurred images, we add a deblurring process before matching. Due to the ill-posedness of the deblurring problem, we use an image sparsity prior combined with patch-wise minimal and maximal pixel of latent image. Half quadratics splitting algorithm is applied under the maximum a posterior (MAP) framework. Six classical feature descriptors are utilized to implement the image matching. Five evaluation indexes are used to test the effect of the proposed deblurring algorithm on motion blurred remote sensing images matching. Experimental results show that the proposed deburring method can impair the influence of motion blur and improve the precision of matching.

With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many existing deep-learning models focus on designing different architectures, only a few works in the RS field have focused on investigating the performance of the learned feature embeddings and the associated metric space. In particular, two main loss functions have been exploited: the contrastive and the triplet loss. However, the straightforward application of these techniques to RS images may not be optimal in order to capture their neighborhood structures in the metric space due to the insufficient sampling of image pairs or triplets during the training stage and to the inherent semantic complexity of remotely sensed data. To solve these problems, we propose a new deep metric learning approach, which overcomes the limitation on the class discrimination by means of two different components: 1) scalable neighborhood component analysis (SNCA) that aims at discovering the neighborhood structure in the metric space and 2) the cross-entropy loss that aims at preserving the class discrimination capability based on the learned class prototypes. Moreover, in order to preserve feature consistency among all the minibatches during training, a novel optimization mechanism based on momentum update is introduced for minimizing the proposed loss. An extensive experimental comparison (using several state-of-the-art models and two different benchmark data sets) has been conducted to validate the effectiveness of the proposed method from different perspectives, including: 1) classification; 2) clustering; and 3) image retrieval. The related codes of this article will be made publicly available for reproducible research by the community.

In this paper, we present an automatic approach for change detection in a large and complex image scenario. The proposed technique takes advantages of automatic registration algorithm and change detection method that jointly measures the spatial invariant but spectral variant features in the considered bitemporal remote sensing image pair. Two classes of pseudo training samples, which associated to the change and no-change two classes, are automatically generated by analyzing the change representation information from both global and local perspectives. Finally, the robust classifier, i.e., linear support vector machine (LSVM), is used to identify the binary changes in the whole image scenario using the pseudo training samples. Experimental results obtained on a pair of real bitemporal Landsat-8 OLI images covering a large scene confirmed the effectiveness of the proposed method.

The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness.

High-resolution satellite images (HRSIs) obtained from linear array charge-coupled device sensors always suffer from geometric instability in the presence of attitude jitter. Therefore, detection and compensation of spacecraft attitude jitter in both the cross-track and along-track directions are crucial to improve the geometric accuracy of HRSIs. A number of reports have been made on the detection and estimation of cross-track attitude jitter. However, the detection of the attitude jitter in the along-track direction is more complicated due to the impact of topographic change. This paper presents a novel approach to achieve accurate estimation of the along-track attitude jitter by eliminating the influence of topographic information based on the back-projection residuals of three-line-array (TLA) images. The principle of detection and estimation of along-track attitude jitter is described, and the proposed approach consists of three main components as follows: 1) dense image matching of the TLA images using a comprehensive matching strategy; 2) detection of the back-projection residuals in the line direction caused by attitude jitter; and 3) estimation of the along-track attitude jitter from the back-projection residuals using a genetic algorithm. Experiments were conducted using China's Ziyuan-3 (ZY-3) TLA images, and the experimental results reveal that the frequency of the attitude jitter in the along-track direction ranges between 0.6 and 0.7 Hz, which is consistent with the frequency in the cross-track direction observed in our previous study. In addition, a comparison of the results of the proposed approach with those from direct attitude observations shows good consistency, with as little as 0.1-pixel disparity, which demonstrates the feasibility and reliability of the proposed approach. Furthermore, the geometric accuracy is further improved from a pixel level to a subpixel level and the periodic trend is removed with the compensation of the estimated attitude jitter in addition to the conventional affine compensation, which validates the potential of the proposed approach for geometric accuracy improvement with ZY-3 TLA images.

In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

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.

Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.