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Wide field of view (WFV) push-broom optical satellites can acquire ground images over hundreds to thousands of kilometers in a single pass, thanks to their large field of view (FOV) cameras consisting of tens or even hundreds of thousands of detectors, which also makes their relative radiometric correction (RRC) difficult. Existing side-slither-based RRC approaches overlook the distinct structural design of WFV push-broom optical satellite cameras, thus failing to rectify nonlinear distortions in side-slither images stemming from the nonflat arrangement of camera detectors. Furthermore, these methods necessitate the corresponding pixels (pixels of the same ground object) covering the entire FOV in side-slither images. However, the brief duration of side-slither imaging makes it unfeasible to cover the entire FOV with calibration data. To address these issues, we propose a novel RRC approach for WFV push-broom optical satellites, achieved based on a thorough analysis of the unique structural traits of WFV push-broom optical cameras, enabling precise standardization of the nonlinear distortions in side-slither data. Additionally, a local-to-global side-slither calibration strategy is proposed to obviate the requirement for corresponding pixels to cover the entire FOV in calibration data. Experiments using the Haiyang-1D Coastal Zone Imager satellite indicate that our method effectively rectified nonlinear distortions in side-slither data, and evident RRC accuracy improvement with that of the existing methods can be obtained.

Hyperspectral images are widely use due to their rich spectral information. Meanwhile, the difficult acquisition of data labels makes unsupervised classification attracts attention. Subspace clustering as an unsupervised classification method is widely used for hyperspectral image analysis because of its excellent performance and robustness. However, conventional subspace clustering does not consider the nonlinear structure of hyperspectral data, and deep subspace clustering tends to ignore the intrinsic structure of hyperspectral data. To address these problems, we developed a self-expressive learning network, ResSENet, for hyperspectral data; we then proposed the application of ResSENet under graph constraints (GC-ResSENet), considering the intrinsic graph structure of the data. Unlike conventional deep subspace clustering, our model discards the self-expressive layer; self-expressive coefficients between datasets are directly solved by the data using network parameters. Hyperparameters are used in the joint loss to balance the self-expressive properties of the data and the graph constraint terms. We evaluated GC-ResSENet by applying it to four well-known datasets, and our network achieved optimal performance. In addition, because of its abandonment of the self-expressive layer, ResSENet is theoretically capable of clustering with large datasets; thus, we evaluated it using two large datasets.

ABSTRACTBlock Adjustment (BA) is a critical procedure in the geometric processing of satellite images, responsible for compensating and correcting the geometric positioning errors of the images. The accuracy of the photogrammetric products, including Digital Orthophoto Map (DOM), Digital Elevation Model (DEM), Digital Line Graphic (DLG), and Digital Raster Graphic (DRG), directly depends on the accuracy of BA results. In recent years, the rapid development of related technologies such as Artificial Intelligence (AI), Computer Vision (CV), Unmanned Aerial Vehicles (UAVs) and big data has greatly facilitated and transformed the classical BA in photogrammetry. This paper first reviews the current status of BA and then looks into the future. First, this paper provides a brief review of the key technologies involved in BA, including image matching, the establishment of adjustment model, the determination of the parameters and the detection of gross error. Then, taking the intercross and fusion of current technologies such as AI, cloud computing and big data with photogrammetry into account, this paper explores the future trends of photogrammetry. Finally, four typical cases of large-scale adjustment are introduced, including large-scale BA without Ground Control Points (GCPs) for optical stereo satellite images, large-scale BA with laser altimetry data for optical stereo satellite images, large-scale BA for UAV oblique photogrammetry, and large-scale BA for indoor photogrammetry in caves with a large number of close-range images.

ABSTRACTBlock Adjustment (BA) is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery. For block adjustment with few ground-control points or without ground control, the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products. The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods, high positioning precision, and low acquisition cost, providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA. This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery (OSSI) and spaceborne laser altimeter data. Firstly, we elaborate the principle and necessity of this work through a literature review of existing methods. Then, the framework of our geo-positioning models. Secondly, four key technologies of the proposed model are expounded in order, including the acquisition and management of global Laser Control Points, the association of LCPs and OSSI, the block adjustment model combining LCPs with OSSI, and the accuracy estimation and quality control of the combined BA. Next, the combined BA experiment using Ziyuan-3 (ZY-3) OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province, China. Experimental results prove that our method can automatically select LCPs with high accuracy. The elevation deviation of the combined BA eventually achieved the Mean Error (ME) of 0.06 m and the Root Mean Square Error (RMSE) of 1.18 m, much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment. A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.

Stereo vision has attracted much interest in computer vision and remote sensing. Large-scale terrain extraction from optical satellites has achieved various applications due to the continuous development of optical satellite technology in the last forty years. This study analyzes the literature sources published in the last decade on stereo vision, optical flow, and remote sensing and finds that applications of the optical flow method in very high resolution (VHR) optical satellite stereo matching are inadequate. Therefore, it proposes a comprehensive optical flow-based terrain extraction framework from optical satellite stereo images. Experiments are conducted using stereo images from four different optical satellites, and the results are compared to the pertinent algorithms of ENVI, ERDAS, and ASP software. The qualitative and quantitative analysis demonstrates that the proposed method has significantly superior matching effect and robustness with a higher accuracy level compared with other approaches, especially for mountainous areas. In addition, the root mean square errors (RMSEs) of elevation on four datasets are 1.228 m, 2.159 m, 3.150 m, and 5.763 m, indicating the feasibility of the proposed method for terrain extraction from optical satellite stereo images.

Correcting the relative geometric errors is an essential step in the generation of digital ortho maps and digital surface models from GaoFen-7 (GF-7) regional images, in which the block adjustment (BA) technology is always adopted. However, the traditional BA methods are typically ineffective and not universal due to their dependence on sufficient control data. Although recently developed BA methods using a large number of observation images can substantially eliminate the additional control data, they are not suitable for the BA with few images in the absence of sufficient control data, which is a situation that is often encountered in practical applications. In such situation, the inevitable elevation error caused by the initial positioning error of images will transfer and accumulate in the horizontal direction of the block, resulting in the inconsistent geometric accuracies among images. Aiming at this problem, this article proposed a practical method of combining a stable free BA and a posteriori compensation. Through the stepwise correction and integration of geometric errors, the elevation error and its cumulative effect in free BA could be eliminated at a small control cost, and a robust result with consistent accuracy could be achieved, even with only a few observation images. This method was validated through experiments performed on GF-7 stereo images, and the satisfactory results indicated that it effectively restrained error accumulation and improved the geo-positioning consistency of regional GF-7 images.

This article proposes an efficient and high-fidelity panchromatic (PAN) spectral decomposition method based on smoothing filtering for multispectral (MS) and hyperspectral (HS) image pansharpening. The proposed method assumes that the high-frequency spatial details at the same scale are the same for different spectral images captured by the satellite at the same time. When the PAN image is prefiltered and down-sampled to the MS scale, it will have the same high-frequency spatial detail as the MS image, with only low-frequency spectral differences. Prefiltering for antialiasing when downsampling. Then, the spectral decomposition coefficients from down-sampled PAN image to MS image can be calculated on MS scale. The low-frequency spectral information of an image at different scales is the same, the spectral decomposition coefficients on the MS scale can be up-sampled to the PAN scale, and the original PAN image can be spectrally decomposed to obtain the sharpened image. The proposed method only decomposes the low-frequency spectrum, and the high-frequency spatial details are the same as the original PAN image, the spatial detail is well preserved. This article verifies the effect of the proposed method on MS and HS image sharpening through experiments, and the results show that the proposed method is better than the comparison method. This article also controls other variables to compare with the HPM method. The results show that the hybrid quality with no reference and spatial distortion index ($D_{s}$) of the proposed method are better than the HPM method.

The paper presents a geometric calibration method based on the sparse ground control points (GCPs), aiming to the linear push-broom optical satellite. This method can achieve the optimal estimate of internal and external parameters with two overlapped image pair along the charge-coupled device (CCD), and sparse GCPs in the image region, further get rid of the dependence on the expensive calibration site data. With the calibrated parameters, the line of sight (LOS) of all CCD detectors can be recovered. This paper firstly establishes the rigorous imaging model of linear push-broom optical satellite based on its imaging mechanism. And then the calibration model is constructed by improving the internal sensor model with a viewing-angle model after an analysis on systematic errors existing in the imaging model is performed. A step-wise solution is applied aiming to the optimal estimate of external and internal parameters. At last, we conduct a set of experiments on the ZY-3 NAD camera and verify the accuracy and effectiveness of the presented method by comparison.

Patch imaging is an important capability of GaoFen4, which is the first Chinese high-resolution planar array satellite in geosynchronous orbit. When the satellite collects images in the patch-imaging mode, overlapping images can be successively obtained. As these images are captured at different times and from a very high orbit, the initial geometric accuracy of the overlapping area between images is inconsistent, which directly affects image mosaicking. In this paper, a novel bundle block adjustment method based on the rational function model without ground control points (GCPs) is proposed to eliminate the relative geometric error among the images and to generate refined rational polynomial coefficients (RPCs). In this method, the average elevation is used instead of the true elevation of the ground points to solve the problem of weak convergence of corresponding bundles, which would result in unstable calculation in the height direction. Virtual control points (VCPs) generated from the original RPCs are used to restrain the freedom of the entire block in the horizontal direction, thereby ensuring stable calculation without the use of GCPs. A successive RPC regeneration method based on VCPs is also presented. To verify the effectiveness of the proposed method, four experiments were performed using real data to assess the geometric accuracy of the method, and the satisfactory experimental results indicate that the presented method is both practical and effective. [ABSTRACT FROM AUTHOR]