Search Results (17)

National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approaches help address this issue by reducing dependence on fully annotated images and leveraging unlabeled data. However, their potential for large-scale LC mapping needs further investigation. This study explored the use of NFI data with deep learning and weakly supervised and self-supervised methods. Using Sentinel-2 images and the Portuguese NFI, which covers other LC types beyond forest, as sparse labels, we performed weakly supervised semantic segmentation with a convolutional neural network to create an updated and spatially continuous national LC map. Additionally, we investigated the potential of self-supervised learning by pretraining a masked autoencoder on 65,000 Sentinel-2 image chips and then fine-tuning the model with NFI-derived sparse labels. The weakly supervised baseline achieved a validation accuracy of 69.60%, surpassing Random Forest (67.90%). The self-supervised model achieved 71.29%, performing on par with the baseline using half the training data. The results demonstrated that integrating both learning approaches enabled successful countrywide LC mapping with limited training data. Full article

The Global Navigation Satellite System (GNSS) provides critical positioning, navigation, and timing (PNT) services worldwide, enabling a wide range of applications from everyday use to advanced scientific and military operations. The importance of Low Earth Orbit (LEO) PNT systems lies in their ability to enhance the GNSS by implementing signals in additional frequency bands, offering increased signal strength, reduced latency, and improved accuracy and coverage, particularly in challenging environments such as urban canyons or polar regions, thereby addressing the limitations of the traditional Medium Earth Orbit (MEO) GNSS. This paper details the system engineering of a novel CubeSat-based multi-regional PNT system tailored for deployment in LEO. The proposed system leverages on a miniaturized CubeSat-compatible PNT payload that includes a chip-scale atomic clock (CSAC) and relies on MEO GNSS technologies to deliver positioning and timing information across multiple regions. The findings indicate that the proposed CubeSat-based PNT system offers a viable solution for enhancing global navigation and timing services, with potential commercial and scientific applications. This work contributes to the growing body of knowledge on LEO-based PNT systems and lays the groundwork for future research and development in this rapidly evolving field. Full article

The GaoFen-14 satellite is primarily utilized for global high-precision positioning and generating 1:10,000 scale geographic information products, making it one of the most accurate stereo mapping satellites in China. With a long-stitched CCD (charge couple device) consisting of nine chips for the forward view camera and six chips for the backward view camera, it is crucial to validate the satellite’s geometric performance and achieve high uncontrolled global positioning precision. In this study, two calibration models were proposed for the on-orbit calibration of the GaoFen-14 satellite: one accounting for the inter-chip geometry constraint and another without the constraint. Correspondingly, five calibration schemes (A, B, C, D, and E) were designed, with varying settings of the external calibration parameters, interior parameters, and utilization of the optical axis measurement data. Schemes A and B utilized the same set of alignment angles for the forward and backward images and differed in the set of interior calibration parameters. Scheme A took a single set of interior calibration parameters for all chips within the same view, while Scheme B set independent interior calibration parameters for each chip in both the forward and backward views considering the inter-chip geometry constraint. Schemes C, D, and E set two groups of alignment angles for the forward and backward views, respectively. In addition, Schemes C, D, and E utilized GaoFen-14′s specific real-time recording data of the optical axis, incorporating a slight revision of the alignment angle calibration value using dichotomous search. The interior models of Schemes A and C were identical, and those of Schemes B and D were the same. In comparison with scheme D, scheme E only lacks the inter-chip geometry constraint. These five schemes were taken to validate the most suitable exterior model and interior model for the GaoFen-14 satellite. Calibration experiments were conducted using original multichip images and the stitched images from the Zhongwei, Ningxia, and Songshan (Henan) test fields. Additionally, four sets of data from around the world are used to verify the calibration effect. The results demonstrated significant improvements in uncontrolled positioning accuracy for all five calibration schemes, transitioning from the 100 m level to the meter or even the submeter level. However, Schemes A and B exhibited unstable controlled positioning accuracy, with fluctuations ranging from 1 m to 5 m. In contrast, Schemes C, D, and E achieved substantial enhancements in uncontrolled positioning accuracy, maintaining stability with variations ranging from 0.94 to 1.14 m in the X direction, 0.41 to 0.48 m in the Y direction, and 0.77 to 0.81 m in the Z direction. The positions of the CCD probes obtained from Schemes C and D demonstrated consistency, with over 80% of the probes falling within 0.1 pixels, and all of the sampled probes within 0.3 pixels. However, Scheme D behaves better in describing the geometric deformation of each CCD chip and keeping the integrity of the long-stitched CCD simultaneously. These experimental results validate the GaoFen-14 satellite’s ability to achieve stable uncontrolled positioning accuracy worldwide and a stable geometric structure between multichips. For the GaoFen-14 satellite, it is more appropriate to adopt two sets of alignment angles for the forward and backward views, respectively. As the satellite’s on-orbit operation time increases, it is recommended to employ Scheme D for monitoring inconsistent local geometric deformation in future calibration work. Full article

Solar sailing enables efficient propellant-free attitude adjustment and orbital maneuvers of solar sail spacecraft with high area-to-mass ratios. However, the heavy supporting mass for large solar sails inevitably leads to low area-to-mass ratios. Inspired by chip-scale satellites, a chip-scale solar sail system named ChipSail, consisting of microrobotic solar sails and a chip-scale satellite, was proposed in this work. The structural design and reconfigurable mechanisms of an electrothermally driven microrobotic solar sail made of Al\Ni₅₀Ti₅₀ bilayer beams were introduced, and the theoretical model of its electro-thermo-mechanical behaviors was established. The analytical solutions to the out-of-plane deformation of the solar sail structure appeared to be in good agreement with the finite element analysis (FEA) results. A representative prototype of such solar sail structures was fabricated on silicon wafers using surface and bulk microfabrication, followed by an in-situ experiment of its reconfigurable property under controlled electrothermal actuation. The experimental results demonstrated significant electro-thermo-mechanical deformation of such microrobotic bilayer solar sails, showing great potential in the development of the ChipSail system. Analytical solutions to the electro-thermo-mechanical model, as well as the fabrication process and characterization techniques, provided a rapid performance evaluation and optimization of such microrobotic bilayer solar sails for the ChipSail. Full article

Since deep space exploration tasks, such as space gravitational wave detection, put forward increasingly higher requirements for the satellite platform, the scale and complexity of the satellite management unit (SMU) software are also increasing, and the trend of intelligentization is showing. It is difficult for the traditional SMU based on single-core system on chip (SoC) to meet the various requirements brought by the above trends. This paper presents a high-performance general-purpose SMU design. Based on rad-hard multi-core SoC, we configure and tailor Linux, and design an SMU software architecture with three modes. It has the characteristics of high performance, high reliability, general purpose and scalability, which can meet the needs of the SMU of future complex satellites. Finally, through the application experiment in the background of the space gravitational wave detection project, the performance and application prospect of our proposed SMU are demonstrated. Full article

The rapid development of remote sensing technology has brought about a sharp increase in the amount of remote sensing image data. However, due to the satellite’s limited hardware resources, space, and power consumption constraints, it is difficult to process massive remote sensing images efficiently and robustly using the traditional remote sensing image processing methods. Additionally, the task of satellite-to-ground target detection has higher requirements for speed and accuracy under the conditions of more and more remote sensing data. To solve these problems, this paper proposes an extremely efficient and reliable acceleration architecture for forward inference of the YOLOX-s detection network an on-orbit FPGA. Considering the limited onboard resources, the design strategy of the parallel loop unrolling of the input channels and output channels is adopted to build the largest DSP computing array to ensure a reliable and full utilization of the limited computing resources, thus reducing the inference delay of the entire network. Meanwhile, a three-path cache queue and a small-scale cascaded pooling array are designed, which maximize the reuse of on-chip cache data, effectively reduce the bandwidth bottleneck of the external memory, and ensure an efficient computing of the entire computing array. The experimental results show that at the 200 MHz operating frequency of the VC709, the overall inference performance of the FPGA acceleration can reach 399.62 GOPS, the peak performance can reach 408.4 GOPS, and the overall computing efficiency of the DSP array can reach 97.56%. Compared with the previous work, our architecture design further improves the computing efficiency under limited hardware resources. Full article

Traditionally, an elevation-angle-dependent weighting method is usually used for Global Navigation Satellite System (GNSS) positioning with a geodetic receiver. As smartphones adopt linearly polarized antenna and low-cost GNSS chips, different GNSS observation properties are exhibited. As a result, a carrier-to-noise ratio (C/N0)-dependent weighting method is mostly used for smartphone-based GNSS positioning. However, the C/N0 is subject to the effects of the observation environment, resulting in an unstable observation weight. In this study, we propose a combined elevation angle and C/N0 weighting method for smartphone-based GNSS precise point positioning (PPP) by normalizing the C/N0-derived variances to the scale of the elevation-angle-derived variances. The proposed weighting method is validated in two kinematic PPP tests with different satellite visibility conditions. Compared with the elevation-angle-only and C/N0-only weighting methods, the combined weighting method can effectively enhance the smartphone-based PPP accuracy in a three-dimensional position by 22.7% and 24.2% in an open-sky area, and by 52.0% and 26.0% in a constrained visibility area, respectively. Full article

In this study, we propose a miniaturized bandpass filter (BPF) developed by combining an approximate circular (36-gon) winding inductor, a circinate capacitor, and five air-bridge structures fabricated on a gallium arsenide (GaAs) substrate using an integrated passive device (IPD) technology. We introduced air-bridge structures into the outer metal wire to improve the capacitance per unit volume while utilizing a miniaturized chip with dimensions 1538 μm × 800 μm (0.029 λ₀ × 0.015 λ₀) for the BPF. The pattern was designed and optimized by simulating different dimensional parameters, and the group delay and current density are presented. The equivalent circuit was modeled to analysis various parasitic effect. Additionally, we described the GaAs-based micro-nano scale fabrication process to elucidate the proposed IPD technology and the physical structure of the BPF. Measurements were conducted with a center frequency of 1.53 GHz (insertion loss of 0.53 dB) and a 3-dB fractional bandwidth (FBW) of 70.59%. The transmission zero was located at 4.16 GHz with restraint of 35.86 dB. Owing to the benefits from its miniaturized chip size and high performance, the proposed GaAs-based IPD BPF was verified as an excellent device for various S-band applications, such as satellite communication, keyless vehicle locks, wireless headphones, and radar. Full article

Global Navigation Satellite Systems’ radio occultation (GNSS-RO) provides the upper troposphere-lower stratosphere (UTLS) vertical atmospheric profiles that are complementing radiosonde and reanalysis data. Such data are employed in the numerical weather prediction (NWP) models used to forecast global weather as well as in climate change studies. Typically, GNSS-RO operates by remotely sensing the bending angles of an occulting GNSS signal measured by larger low Earth orbit (LEO) satellites. However, these satellites are faced with complexities in their design and costs. CubeSats, on the other hand, are emerging small and cheap satellites; the low prices of building them and the advancements in their components make them favorable for the GNSS-RO. In order to be compatible with GNSS-RO requirements, the clocks of the onboard receivers that are estimated through the precise orbit determination (POD) should have short-term stabilities. This is essential to correctly time tag the excess phase observations used in the derivation of the GNSS-RO UTLS atmospheric profiles. In this study, the stabilities of estimated clocks of a set of CubeSats launched for GNSS-RO in the Spire Global constellation are rigorously analysed and evaluated in comparison to the ultra-stable oscillators (USOs) onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites. Methods for improving their clock stabilities are proposed and tested. The results (i) show improvement of the estimated clocks at the level of several microseconds, which increases their short-term stabilities, (ii) indicate that the quality of the frequency oscillator plays a dominant role in CubeSats’ clock instabilities, and (iii) show that CubeSats’ derived UTLS (i.e., tropopause) atmospheric profiles are comparable to those of COSMIC-2 products and in situ radiosonde observations, which provided external validation products. Different comparisons confirm that CubeSats, even those with unstable onboard clocks, provide high-quality RO profiles, comparable to those of COSMIC-2. The proposed remedies in POD and the advancements of the COTS components, such as chip-scale atomic clocks and better onboard processing units, also present a brighter future for real-time applications that require precise orbits and stable clocks. Full article

In the era of big data, where massive amounts of remotely sensed imagery can be obtained from various satellites accompanied by the rapid change in the surface of the Earth, new techniques for large-scale change detection are necessary to facilitate timely and effective human understanding of natural and human-made phenomena. In this research, we propose a chip-based change detection method that is enabled by using deep neural networks to extract visual features. These features are transformed into deep orthogonal visual features that are then clustered based on land cover characteristics. The resulting chip cluster memberships allow arbitrary level-of-detail change analysis that can also support irregular geospatial extent based agglomerations. The proposed methods naturally support cross-resolution temporal scenes without requiring normalization of the pixel resolution across scenes and without requiring pixel-level coregistration processes. This is achieved with configurable spatial locality comparisons between years, where the aperture of a unit of measure can be a single chip, a small neighborhood of chips, or a large irregular geospatial region. The performance of our proposed method has been validated using various quantitative and statistical metrics in addition to presenting the visual geo-maps and the percentage of the change. The results show that our proposed method efficiently detected the change from a large scale area. Full article

GNSS (global navigation satellite system) and SINS (strap-down inertial navigation system) integrated navigation systems have been the apparatus for providing reliable and stable position and velocity information (PV). Commonly, there are two solutions to improve the GNSS/SINS integration navigation system accuracy, i.e., employing GNSS with higher position accuracy in the integration system or utilizing the high-grade inertial measurement unit (IMU) to construct the integration system. However, technologies such as RTK (real-time kinematic) and PPP (precise point positioning) that improve GNSS positioning accuracy have higher costs and they cannot work under high dynamic environments. Also, an IMU with high accuracy will lead to a higher cost and larger volume, therefore, a low-cost method to enhance the GNSS/SINS integration accuracy is of great significance. In this paper, multiple receivers based on the GNSS/SINS integrated navigation system are proposed with the aim of providing more precise PV information. Since the chip-scale receivers are cheap, the deployment of multiple receivers in the GNSS/SINS integration will not significantly increase the cost. In addition, two different filtering methods with central and cascaded structure are employed to process the multiple receivers and SINS integration. In the centralized integration filter method, measurements from multiple receivers are directly processed to estimate the SINS errors state vectors. However, the computation load increases heavily due to the rising dimension of the measurement vector. Therefore, a cascaded integration filter structure is also employed to distribute the processing of the multiple receiver and SINS integration. In the cascaded processing method, each receiver is regarded as an individual “sensor”, and a standard federated Kalman filter (FKF) is implemented to obtain an optimal estimation of the navigation solutions. In this paper, a simulation and a field tests are carried out to assess the influence of the number of receivers on the PV accuracy. A detailed analysis of these position and velocity results is presented and the improvements in the PV accuracy demonstrate the effectiveness of the proposed method. Full article

With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are available than ever before, thus making dynamic ship monitoring possible. Object detectors in deep learning achieve top performance, benefitting from a free public dataset. Unfortunately, due to the lack of a large volume of labeled datasets, object detectors for SAR ship detection have developed slowly. To boost the development of object detectors in SAR images, a SAR dataset is constructed. This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 43,819 ship chips of 256 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. Moreover, modified state-of-the-art object detectors from natural images are trained and can be used as baselines. Experimental results reveal that object detectors achieve higher mean average precision (mAP) on the test dataset and have high generalization performance on new SAR imagery without land-ocean segmentation, demonstrating the benefits of the dataset we constructed. Full article

Radiometric calibration for imaging sensors is a crucial procedure to ensure imagery quality. One of the challenges in relative radiometric calibration is to correct detector-level artifacts due to the fluctuation in discrepant responses (spatial) and electronic instability (temporal). In this paper, the integration of the empirical mode decomposition (EMD) with Hilbert–Huang transform (HHT) in relative radiometric calibration was explored for a new sensor, FS-5 RSI (remote sensing instrument onboard the FORMOSAT-5 satellite). The key intrinsic mode functions (IMFs) analyzed by HHT were examined with the pre-flight datasets of the FS-5 RSI in temporal and spatial variations. The results show that the EMD–HHT method can stabilize and improve the radiometric quality of the FS-5 imagery as well as boost its application ability to a new level. It is noticed that the IMFs of the spatial variation would be disturbed by the instability of the temporal variation. The relative response discrepancies among detector chips can be well calibrated after considering the temporal effect. Taking a test imagery dataset of gain setting G2 as an example, the standard deviation (STD) of the discrepancy in the digital number after calibration was dramatically scaled down compared to the original ones (e.g., PAN: 66.31 to 1.85; B1: 54.19 to 1.90; B2: 36.50 to 1.49; B3: 32.43 to 1.56; B4: 37.67 to 1.20). The good performance of pre-flight imagery indicates that the EMD–HHT approach could be highly practical to the on-orbit relative radiometric calibration of the FS-5 RSI sensor and is applicable to other optical sensors. To our knowledge, the proposed EMD–HHT approach is used for the first time to explore relative radiometric calibration for optical sensors. Full article

With the development of satellite load technology and very large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. A key goal of the on-board SAR imaging system design is to achieve high real-time processing performance under severe size, weight, and power consumption constraints. This paper presents a multi-node prototype system for real-time SAR imaging processing. We decompose the commonly used chirp scaling (CS) SAR imaging algorithm into two parts according to the computing features. The linearization and logic-memory optimum allocation methods are adopted to realize the nonlinear part in a reconfigurable structure, and the two-part bandwidth balance method is used to realize the linear part. Thus, float-point SAR imaging processing can be integrated into a single Field Programmable Gate Array (FPGA) chip instead of relying on distributed technologies. A single-processing node requires 10.6 s and consumes 17 W to focus on 25-km swath width, 5-m resolution stripmap SAR raw data with a granularity of 16,384 × 16,384. The design methodology of the multi-FPGA parallel accelerating system under the real-time principle is introduced. As a proof of concept, a prototype with four processing nodes and one master node is implemented using a Xilinx xc6vlx315t FPGA. The weight and volume of one single machine are 10 kg and 32 cm × 24 cm × 20 cm, respectively, and the power consumption is under 100 W. The real-time performance of the proposed design is demonstrated on Chinese Gaofen-3 stripmap continuous imaging. Full article

Chip Scale Atomic Clocks (CSAC) are recently-developed electronic instruments that, when used together with a Global Navigation Satellite Systems (GNSS) receiver, help improve the performance of GNSS navigation solutions in certain conditions (i.e., low satellite visibility). Current GNSS receivers include a Temperature Compensated Cristal Oscillator (TCXO) clock characterized by a short-term stability (τ = 1 s) of 10⁻⁹ s that leads to an error of 0.3 m in pseudorange measurements. The CSAC can achieve a short-term stability of 2.5 × 10⁻¹² s, which implies a range error of 0.075 m, making for an 87.5% improvement over TCXO. Replacing the internal TCXO clock of GNSS receivers with a higher frequency stability clock such as a CSAC oscillator improves the navigation solution in terms of low satellite visibility positioning accuracy, solution availability, signal recovery (holdover), multipath and jamming mitigation and spoofing attack detection. However, CSAC suffers from internal systematic instabilities and errors that should be minimized if optimal performance is desired. Hence, for operating CSAC at its best, the deterministic errors from the CSAC need to be properly modelled. Currently, this modelling is done by determining and predicting the clock frequency stability (i.e., clock bias and bias rate) within the positioning estimation process. The research presented in this paper aims to go a step further, analysing the correlation between temperature and clock stability noise and the impact of its proper modelling in the holdover recovery time and in the positioning performance. Moreover, it shows the potential of fine clock coasting modelling. With the proposed model, an improvement in vertical positioning precision of around 50% with only three satellites can be achieved. Moreover, an increase in the navigation solution availability is also observed, a reduction of holdover recovery time from dozens of seconds to only a few can be achieved. Full article