Soft pneumatic robots are gaining significant attention due to their compliance and adaptability in unstructured environments. While emerging dual-chamber soft pneumatic robots can achieve complex 3D deformations beyond conventional single-axis bending, real-time proprioception remains challenging due to the high degrees of freedom and the complex interaction between chambers. To address this issue, we propose a multimodal learning-based sensing method that combines camera and inertial measurement unit (IMU) and then extracts full-body shape information using deep learning algorithms. Our method enhances proprioception by effectively processing high-dimensional sensor data, providing real-time feedback on the gripper shape. The average error of key points was found to be 3.67mm (Var 8.39) for our method, while the error was 4.36mm (Var 10.47) when a camera was used alone, or 9.32mm (Var 21.29) when an IMU was used alone. Our multimodal learning-based shape estimation and reconstruction empower soft pneumatic grippers to be seamlessly integrated into the embodied AI framework, significantly improving their reliability and thus paving the way for applications in service robotics, ehabilitation robotics, and human-robot collaborations.

Most of the existing video synthetic aperture radar (ViSAR) vehicle multitarget tracking (MTT) methods only perform interframe association based on the idea of appearance modeling, and are not closely integrated with the ViSAR moving target imaging characteristics, resulting in limited accuracy improvement of existing MTT methods. ViSAR moving targets have the characteristics of individual similarity, time-varying appearance, and background pseudo-motion, which have a great impact on tracking performance. In this regard, we propose a multivehicle tracking method for ViSAR with reliable foreground-background motion feature compensation (RFBMFC). Specifically, in order to improve the distinguishability of individual features, the spatial-temporal semantic sparse alignment (STSSA) module with intraframe and interframe context key information aggregation and interaction is constructed in the feature extraction stage, which can generate more accurate dense optical flow to enhance the detection and association of foreground targets. In order to improve the tracking continuity of foreground targets with time-varying appearance, the shadow-observation-state mining (SOSM) module is designed in the interframe association stage, which can cluster targets under different appearance states and adaptively restore lost target trajectories. In addition, the background motion fast compensation (BMFC) module is designed, which can learn background motion estimation and correct the trajectory prediction error of foreground targets in an end-to-end self-supervised manner to improve the MTT accuracy under camera motion. Tests on datasets captured by Sandia National Laboratories (SNL) and Beijing Institute of Radio Measurement (BIRM) show that RFBMFC outperforms many representative MTT methods. Compared with the suboptimal method, RFBMFC improves the multiobject tracking accuracy (MOTA) by 1.10% on the SNL data, and by 5.00% on the BIRM data, verifying the effectiveness of RFBMFC.

The features of moving targets in satellite videos are sparse and similar, resulting in two major challenges for multi-target tracking: detection losses and association errors. The rich spatial-temporal proximity and topological relationships among targets in satellite videos can indicate the feature enhancement of small targets and generate discriminative individual descriptions of targets, which helps to improve the accuracy of multi-target tracking. In this article, we propose a novel Multi-target Tracking method for satellite videos guided by Spatial-Temporal Proximity and Topological Relationships (MTT-STPTR). Specifically, a spatial-temporal relationship sparse attention (STRSA) module is constructed in the feature extraction stage to accurately enhance the feature expression of small targets by capturing the cross-frame semantics relevant to the target areas. In addition, a joint feature matching (JFM) module is designed in the interframe association stage, which constructs a novel similarity measurement method of star-shaped topological structures and uses it to measure the similarity of multidimensional features of target individuals, thereby alleviating association errors caused by dense individuals with similar features. Moreover, a novel multiple granularity spatial-temporal contrastive learning (MGSTCL) module is designed to promote a balanced optimization of detection and association tasks for multicategory targets in satellite videos. Experiments conducted on two public datasets, VISO and AIR-MOT, demonstrate that MTT-STPTR outperforms existing state-of-the-art multi-target tracking methods in terms of the multiobject-tracking accuracy (MOTA) and identification$F1$-score (IDF1), indicating its effectiveness.

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

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

Video synthetic aperture radar (Video SAR) has sparked lots of research attention since it provides the ability of continuously observing and tracking the object of interest. Instead of tracking a target directly, in Video SAR, it is preferred to track its shadow due to the target's instable backscattering characteristic and defocused patterns. However, the current tracking algorithms do not work well since they always suffer from the interference caused by surrounding clutters and cannot locate the target precisely. To address these issues, we propose a robust shadow tracking algorithm for Video SAR in this letter. We equip our tracker with the spatial-temporal (ST) information and saliency-based detection mechanism (SD) against the distractions and background clutters. The proposed optimization formula could be solved efficiently using the alternating direction method of multipliers (ADMM) technique and fully carried in the Fourier domain at a low computational burden. Experiments have demonstrated that the proposed algorithm performs favorably against other state-of-the-art methods.

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

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

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

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.