Search Results (22)

Long-distance navigation requires global positioning methods to have complete autonomy, particularly when the Global Positioning System is unavailable. Considering that bionic polarized light-based global positioning technology exhibits good autonomy, this study develops an error model for autonomous global positioning based on the polarized skylight and a true north measurement instrument, using an approach of partial derivatives. The proposed model can rapidly and accurately provide the global error distribution of a bionic positioning method under varying angular measurement errors at different times. In addition, the conditions under which the proposed error model remains valid are investigated. The results indicate that the investigation can be simplified to verify whether the denominators of four partial derivatives of an implicit function system are simultaneously non-zero. The accuracy of the proposed error model is verified through numerical simulations. The results indicate that when the deviations of the two independent variables are up to 0.0001°, the positioning error mostly remains less than 14 m. In contrast, fewer geographical locations have positioning errors approaching positive infinity. By analyzing the global error distribution, one can effectively design and optimize the parameters of the autonomous global positioning system, enhancing its reliability and stability. Full article

Traditional single neural network-based geo-localization methods for cross-view imagery primarily rely on polar coordinate transformations while suffering from limited global correlation modeling capabilities. To address these fundamental challenges of weak feature correlation and poor scene adaptation, we present a novel framework termed ICT-Net (Integrated CNN-Transformer Network) that synergistically combines convolutional neural networks with Transformer architectures. Our approach harnesses the complementary strengths of CNNs in capturing local geometric details and Transformers in establishing long-range dependencies, enabling comprehensive joint perception of both local and global visual patterns. Furthermore, capitalizing on the Transformer’s flexible input processing mechanism, we develop an attention-guided non-uniform cropping strategy that dynamically eliminates redundant image patches with minimal impact on localization accuracy, thereby achieving enhanced computational efficiency. To facilitate practical deployment, we propose a deep embedding clustering algorithm optimized for rapid parsing of geo-localization information. Extensive experiments demonstrate that ICT-Net establishes new state-of-the-art localization accuracy on the CVUSA benchmark, achieving a top-1 recall rate improvement of 8.6% over previous methods. Additional validation on a challenging real-world dataset collected at Beihang University (BUAA) further confirms the framework’s effectiveness and practical applicability in complex urban environments, particularly showing 23% higher robustness to vegetation variations. Full article

The objective of this study is to model backscattering coefficients over bare soils at intra-plot spatial scales (from almost 80 to 2800 m²), in a context where the plot is the reference spatial scale in most past studies. A statistical modeling approach, based on a random forest algorithm, is proposed to overcome the limits of semi-empirical or physical models pointed out in the literature and to reduce discrepancies observed between the satellite-derived backscattering coefficients and the predicted values. The experimental device was set up on a network of agricultural plots located in southwestern France during the Multispectral Crop Monitoring (MCM) experiment. The dataset combines high spatial resolution satellite images (acquired by TerraSAR-X and Radarsat-2) together with synchronous geo-located measurements of key soil parameters (i.e., top soil moisture, surface roughness, and soil texture) on consistent spatial areas. Backscattering coefficients are estimated at six intra-plot spatial scales (from ~80 to ~2800 m²), showing an exponential increase in modeling performance, and reaching higher levels of accuracy than previous work performed at the plot spatial scale (i.e., 50% of variance explained in the literature, in the best cases). The increase in signal quality with the spatial scale mainly explains the higher performance observed in the 2800 m² area, with a correlation of 0.91 and RMSE of 0.83 dB in the X-band (for backscattering coefficients acquired with the HH polarization state). In the C-band, the values of correlation range from 0.74 to 0.80, and the RMSE from 1.65 to 1.85 dB (depending on the considered polarization state). The results also showed that the developed statistical algorithm is mainly influenced by the surface roughness and the top soil moisture, as semi-empirical or physical-based models. Soil texture does not significantly affect the algorithm. Full article

To accommodate user-specific requirements and preferences, a travel Recommendation System (RS) gives a customized place of interest. The prevalent research did not provide solutions to some essential situations for cultural tourism, including relevant time, environmental conditions, and stay places. Thus, the existing RS models led to unreliable cultural tourism recommendations by neglecting essential factors like personalized itineraries, environmental conditions of the cultural sites, sentiment analysis of the hotel reviews, and sustainable cultural heritage planning. To overcome the above factors, a day- and night-time cultural tourism RS utilizing the Mean Signed Error-centric Recurrent Neural Network (MSE-RNN) is proposed in this paper. The proposed work develops an efficient RS by considering historical data, Geographic Information System (GIS) map location, hotel (stay place) reviews, and environmental data to access day and night cultural tourism. First, from the Geographic Information System (GIS) map and hotel data, the historical and hotel geolocations are extracted. Currently, these locations are fed to Similarity-centric Hamilton Distance-K-Means (SHD-KM) for grouping the nearest locations. Next, hotels are ranked utilizing the Tent Mapping-centric Black Widow Optimization (TM-BWO) approach centered on the locations. In addition, using Bidirectional Encoder Representations from Transformers (BERT), the essential keywords from the historical geo-locations are embedded. In the meantime, the sites’ reviews and timings are extracted from Google. The extracted reviews go through pre-processing, and the keywords from the pre-processed data are extracted. For the extracted keywords, polarity is calculated centered on the Valence-Aware Dictionary for Sentiment Reasoning (VADER). Concurrently, utilizing the Reference-centric Pearson Correlation Coefficient (R-PCC), the timings of the sites are segregated. Lastly, for providing a recommendation of tourist sites, the embedded words, ranked hotels, and segregated timings, along with the pre-processed environment and season data, are fed to the MSE-RNN classifier. At last, the experimental evaluation verified that other recommendation systems were surpassed by the proposed approach. Full article

This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones. Full article

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-localization algorithm proposed in this study yields results of 92.08%, 97.70%, and 98.66% for the top 1, top 5, and top 10 metrics, respectively, in CVUSA, a popular public cross-view dataset, and exhibits superior performance compared to algorithms of the same type. Full article

Polarimetry, which seeks to measure the vectorial information of light modulated by objects, has facilitated bionic geolocation and navigation applications. It is a novel and promising field that provides humans with a remote sensing tool to exploit polarized skylight in a similar way to polarization-sensitive animals, and yet few in-depth reviews of the field exist. Beginning with biological inspirations, this review mainly focuses on the characterization, measurement, and analysis of vectorial information in polarimetry for bionic geolocation and navigation applications, with an emphasis on Stokes–Mueller formalism. Several recent breakthroughs and development trends are summarized in this paper, and potential prospects in conjunction with some cutting-edge techniques are also presented. The goal of this review is to offer a comprehensive overview of the exploitation of vectorial information for geolocation and navigation applications as well as to stimulate new explorations and breakthroughs in the field. Full article

GPS has become an attractive feature for geolocalization enabling asset tracking IoT devices. GPS satellite antennas radiate RHCP (right-hand circularly polarized) electromagnetic waves; thus, the typical antenna at the receiver is also RHCP. However, when the orientation of the receiving device is random, linear polarization antennas operate better in terms of TTFF (time to first fix). Through field measurements (urban and field) and considering different positions of the device in a vehicle, an RHCP microstrip patch antenna and a linear non-resonant antenna element called an antenna booster were compared. TTFF averaged for several positions was 7 s better for the linearly polarized antenna booster than for the microstrip RHCP patch antenna. The results demonstrate that the behavior of the linear polarization antenna booster technology is more robust in terms of TTFF to the arbitrary position of the IoT device while keeping a small size and simplicity.sdf Full article

Pelagic seabirds cover large distances efficiently and thus may reach a variety of marine habitats during breeding. Previous studies using stable isotope data and geolocators suggested that Thin-billed Prions breeding in the Falkland Islands in the Southwest Atlantic may forage in temperate waters over the Patagonian Shelf or cross the Drake Passage to forage in Antarctic waters south of the Polar Front. We deployed miniature GPS dataloggers to track Thin-billed prions in the Falkland Islands during incubation (3 seasons) and chick-rearing (2 seasons). Thin-billed Prions had a wide distribution during incubation, covering latitudes between 43 and 60° S, with trip lengths of ca. 2000 km over seven days, on average. Thin-billed Prions from two nearby sites (60 km apart) were spatially segregated in their incubation trips, with New Island Thin-billed Prions foraging over the Patagonian Shelf, compared to Thin-billed Prions from Bird Island, that foraged in the region of the Polar Front. During chick-rearing, Thin-billed Prions from New Island undertook both long trips to the Patagonian Shelf and south of the Polar Front (30% of trips were 5–11 days), and short trips (70% of trips were 1–4 days) when they foraged more locally, including in inshore waters around the Falkland Islands. Females carried out more trips to distant sites. Thus, Thin-billed showed a high flexibility in foraging areas, habitats and foraging trip durations, which enable them to benefit from both, temperate and Antarctic environments. Full article

The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km). Full article

The first innovative Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been in operation for 10 years since its launch on 28 October 2011. The second VIIRS sensor aboard the first Join Polar Satellite System (JPSS-1) satellite has been in operation for 4 years since its launch on 18 November 2017, which became NOAA-20. Well-geolocated and radiometrically calibrated Level-1 sensor data records (SDRs) from VIIRS are crucial to numerical weather prediction (NWP) and Level-2+ environmental data record (EDR) algorithms and products. The high quality of Level-2+ EDRs is a requirement for the continuity of NASA Earth science data records (ESDRs) and climate data records (CDRs), one of the two objectives of the SNPP mission and one of the three elements in the JPSS mission objective. The other objective of the SNPP mission is risk reduction for the follow-on JPSS missions. This paper summarizes the on-orbit geolocation calibration and validation (Cal/Val) activities for both VIIRS sensors onboard SNPP and NOAA-20 in the past 10 years. These activities include nominal geolocation Cal/Val activities, risk reduction activities, and improvements for the on-orbit VIIRS sensor operations. After these activities, sub-pixel geolocation accuracy is achieved. Nadir equivalent geolocation uncertainty is generally within 75 m (1-σ), or 20% imagery band pixels, in either the along-scan or along-track direction for both SNPP and NOAA-20 VIIRS sensors. The worst 16-day measured geolocation errors (radial, 3-σ) are 280 m and 267 m, respectively, in the latest SNPP and NOAA-20 VIIRS data collections, which are better than the required accuracy of 375 m (radial, 3-σ). The risk reduction activities also improved VIIRS builds for JPSS-3 and JPSS-4 satellites, and provide lessons learned for other VIIRS-like sensor builds. Full article

Onboard both the Suomi National Polar-orbiting Partnership and Joint Polar Satellite System (JPSS) series of satellites, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) is a new generation of a total ozone column sensor and is used to generate total column ozone products. This study presents a method for efficiently assessing OMPS-NM geolocation accuracy using spatially collocated radiance measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Band M1 by taking advantage of its high spatial resolution (750 m at nadir) and accurate geolocation. The basic idea is to find the best collocation position with maximum correlation between VIIRS collocated and real OMPS-NM radiances by perturbing OMPS-NM line-of-sight (LOS) vectors in the cross-track and along-track directions with small steps in the spacecraft coordinate. The perturbation angles at the best collocation position where OMPS-NM and VIIRS are optimally aligned are used to characterize OMPS-NM geolocation accuracy. In addition, the assessment results can be used to optimize the OMPS-NM field view angle lookup table in the Sensor Data Record (SDR) processing software to improve its geolocation accuracy. To demonstrate the methodology, the proposed method is successfully employed to evaluate OMPS-NM geolocation accuracy with different spatial resolutions. The results indicate that, after the view angle table was updated, the geolocation accuracy for both SNPP and NOAA-20 OMPS-NM is on the sub-pixel level (less than ¼ pixel size) along all the scan positions in both cross-track and along-track directions and the performance is very stable with time. The method proposed in this study lays down the framework for assessing the geolocation accuracy of future high-resolution OMPS-NM measurements. Full article

Ice Pathfinder (Code: BNU-1), launched on 12 September 2019, is the first Chinese polar observation microsatellite. Its main payload is a wide-view camera with a ground resolution of 74 m at the subsatellite point and a scanning width of 744 km. BNU-1 takes into account the balance between spatial resolution and revisit frequency, providing observations with finer spatial resolution than Terra/Aqua MODIS data and more frequent revisits than Landsat-8 OLI and Sentinel-2 MSI. It is a valuable supplement for polar observations. Geolocation is an essential step in satellite image processing. This study aims to geolocate BNU-1 images; this includes two steps. For the first step, a geometric calibration model is applied to transform the image coordinates to geographic coordinates. The images calibrated by the geometric model are the Level1A (L1A) product. Due to the inaccuracy of satellite attitude and orbit parameters, the geometric calibration model also exhibits errors, resulting in geolocation errors in the BNU-1 L1A product. Then, a geometric correction method is applied as the second step to find the control points (CPs) extracted from the BNU-1 L1A product and the corresponding MODIS images. These CPs are used to estimate and correct geolocation errors. The BNU-1 L1A product corrected by the geometric correction method is processed to the Level1B (L1B) product. Although the geometric correction method based on CPs has been widely used to correct the geolocation errors of visible remote sensing images, it is difficult to extract enough CPs from polar images due to the high reflectance of snow and ice. In this study, the geometric correction employs an image division and an image enhancement method to extract more CPs from the BNU-1 L1A products. The results indicate that the number of CPs extracted by the division and image enhancements increases by about 30% to 182%. Twenty-eight images of Antarctica and fifteen images of Arctic regions were evaluated to assess the performance of the geometric correction. The average geolocation error was reduced from 10 km to ~300 m. In general, this study presents the geolocation method, which could serve as a reference for the geolocation of other visible remote sensing images for polar observations. Full article

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily intended for weather related prediction and applications, however, in order to meet the requirements for climate application, further reprocessing must be conducted to first eliminate any potential satellites biases. After the geolocation and cross-scan bias corrections were applied to the dataset, follow-on research focused on the comparison amongst AMSU-A window channels (e.g., 23.8, 31.4, 50.3 and 89.0 GHz) from the six different satellites to remove any inter-satellite inconsistency. Inter-satellite differences can arise from many error sources, such as bias drift, sun-heating-induced instrument variability in brightness temperatures, radiance dependent biases due to inaccurate calibration nonlinearity, etc. The Integrated microwave inter-calibration approach (IMICA) approach was adopted in this study for inter-satellite calibration of AMSU-A window channels after the appropriate standard deviation (STD) thresholds were identified to restrict Simultaneous Nadir Overpass (SNO) data for window channels. This was a critical step towards the development of a set of fundamental and thematic climate data records (CDRs) for hydrological and climatological applications. NOAA-15 served as the main reference satellite for this study. For ensuing studies that expand to beyond 2015, however, it is recommended that a different satellite be adopted as the reference due to concerns over potential degradation of NOAA-15 AMSU-A. Full article

ICESat-2 is the new generation of NASA’s ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean $R^2$ value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2. Full article