Digital watermarking has become a critical technology for copyright protection of digital images. However, the removal of such watermarks to restore original image content presents significant challenges. Current deep learning methods, which rely on GAN or multi-stage encoder-decoder networks, struggle to restore the clean and apparent texture of background images accurately, leading to visual artifacts and degradation in color and texture. Additionally, these methods require precise prediction of watermark positions, limiting their flexibility in handling inaccurate mask predictions.In this paper, we introduce a novel framework, Diffusion Watermark Removal (DiffWR), which employs a diffusion-based approach to address the limitations of existing techniques. DiffWR utilizes Denoising Diffusion Probabilistic Models (DDPM) to generate high-quality, artifact-free restored images. It incorporates an Initial Prediction Module (IPM) to provide preliminary image predictions and guide the diffusion process within accurate boundaries using a binary mask. This approach allows for adaptive inpainting beyond the initially predicted regions, enhancing the flexibility of the restoration process.Our model demonstrates superior performance in terms of visual quality and structural similarity without introducing artifacts. It outperforms state-of-the-art methods in perceptual metrics such as FID and LPIPS, as well as in traditional metrics like SSIM and PSNR. The results showcase the capability of DiffWR to effectively remove watermarks while preserving the original image’s visual fidelity and texture.

Text-to-Image person re-identification (TIReID) aims to retrieve pedestrian images from a gallery based on textual descriptions, thus bridging vision and language modalities for practical retrieval scenarios. Despite recent advances leveraging various cross-modal alignment strategies, existing methods typically assume all image-text pairs in training datasets are correctly matched, overlooking the pervasive Noisy Correspondence (NC) problem—erroneous image-text associations that degrade model robustness. Prior approaches either lack noise identification mechanisms or rely on direct filtering of detected noisy samples, which only partially mitigates the adverse effects of noise and cannot fully prevent overfitting to incorrect correspondences during training. Addressing this challenge, we propose Robust Dual Embedding Contrastive Learning (RDECL), which consists of two main components: 1) A Dual-View Cumulative Trust Division (DCTD) progressively constructs a high-confidence clean sample repository via adaptive sample selection, ensuring reliable image-text correspondence learning under uncertain noise detection.2) A Robust Generalized Contrastive Loss (RGCL) further enhances robustness by leveraging all negative samples and maximizing the loss distribution discrepancy between clean and noisy samples, thereby suppressing overfitting to noisy labels. We conduct extensive experiments on three public benchmark datasets, namely CUHK-PEDES, ICFG-PEDES, and RSTPReID, to evaluate the performance and robustness of our RDECL.

The affine projection sign algorithm (APSA) has garnered significant attention in adaptive filtering due to its exceptional robustness and reduced computational demands. Nevertheless, the inherent use of a fixed projection order in APSA can compromise filtering accuracy and convergence speed. To address this issue, we introduce an innovative strategy for dynamically updating the projection order, resulting in an enhanced version of APSA called the evolving order based APSA (E-APSA). This evolving strategy compares the instantaneous power of output error to a threshold determined by the steady-state mean-square error of APSA, thereby enabling variable projection orders. Furthermore, we provide computational complexity and convergence analyses for E-APSA. Simulation results demonstrate that, compared to other related algorithms, E-APSA offers a significantly faster convergence rate while maintaining competitive steady-state misalignment.

In this paper, jointly with the exponential weighted generalized maximum correntropy (GMC) criterion and the linear constraint framework, we derive a recursive constrained adaptive filtering algorithm named recursive constrained GMC with forgetting factor (FF-RCGMC). In addition, due to a lack of constraint information during the learning process, FF-RCGMC will diverge or even fail to work after some iterations. Therefore, we propose a more stable version by introducing a constraint-forcing strategy into FF-RCGMC and call this robust type as constraint-forcing FF-RCGMC (CFFF-RCGMC). Some simulation results in system identification under non-Gaussian noisy environments validate the effectiveness of CFFF-RCGMC.

In robust adaptive filtering, the affine projection sign algorithm (APSA) is widely used in practice due to the desirable convergence behavior and low computational cost. Several variants of APSA have been derived from the variable step-size method and the combination strategy. For APSA, to further improve the filtering performance, this paper proposes a new filtering performance-enhanced (APSA) by optimizing a weighted cost function. Specifically, motivated by individual-weighting factors, our proposed algorithm, i.e., IWF-APSA, uses an individual-weighting factor to a corresponding input signal, while it has similar computational complexity to APSA. We also conduct the mean-square convergence analysis of IWF-APSA. The simulation results show that IWF-APSA achieves a better filtering performance in terms of convergence rate and filtering accuracy in system identification.

Hyperspectral LIDAR (HSL) is an innovative active remote sensing technology that allows for the simultaneous collection of spectral and spatial information. In this study, we primarily focus on the radiation correction method of the incident angle and distance effects for the backscatter intensity of HSL. We have developed a comprehensive radiometric correction model that addresses these effects. Additionally, we have applied the correction model to point cloud classification using the random forest method. Comparing the accuracy of point cloud classification before and after correction, we observed a 9.6% improvement in overall accuracy (OA) and a 10.8% improvement in the kappa coefficient. These results indicate that the radiometric correction model significantly enhances the classification accuracy.

Convolutional neural networks (CNN) are capable of extracting features with high accuracy, which is dominant in visual-based classification. Previous researches demonstrate that CNN can extract essential features of the target in the plant feature extraction and classification. Hyperspectral LIDAR (HSL) is a novel active remote sensing technology that can simultaneously collect spectral and spatial information. This paper proposed a novel classification method named VI-CNN for hyperspectral LiDAR, which combines the spectral features with the vegetable index(VI). As far as we know, we are the first to apply CNN to HSL data classification. The VI -CNN is divided into two parts. Firstly, spectral CNN focuses on intra-spectral correlations; secondly, the vegetation indices supplement the biological parameters. The evaluation shows that the concatenation has stronger identification and robustness than standalone methods. The experimental results demonstrate that the VI-CNN significantly improves the classification accuracy against other traditional machine-learning methods.

When using a field calibration site to perform in-orbit radiometric calibration of a space-borne remote sensor, the measured signal by the sensor may contain radiation from adjacent pixels, due to scattering in the Earth’s atmosphere and the sensor viewing characteristics. If this is not accounted for in the modeling, the accuracy of the radiometric calibration will be reduced. Nonuniformities of the ground target are more significant for the calibration of high-resolution sensors. In addition, if the brightness contrast of neighboring targets is not small, the impact of the adjacency effects will be more significant. It is important to quantitatively analyze and estimate the influence of this kind of adjacency effects, to reduce the uncertainty of in-orbit radiometric calibration. To evaluate the adjacency effects caused by atmospheric multiple scattering, this article constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data and developed an adjacency effects simulation method, which considers background reflectance spectral information. Tests on Sentinel-2A and Worldview-3 imagery overpassing the Baotou calibration and validation site (Baotou C&V site) (China) indicate that the proposed modeling method can effectively account for the influence of the adjacency effects in vicarious calibration. Uncertainties of relevant parameters and their contributions to the calibration result were also analyzed, and uncertainty assessment results show that the vicarious radiometric calibration scheme considering adjacency effects correction can bring about a total uncertainty less than 7%.

This paper presents a series of temporal vicarious radiometric calibration results of ZY3-MUX sensor over 2016–2019 using the reflectance-based approach. The synchronous ground measuring data have been collected from Baotou sandy site in China given that its high-frequency standard product including surface reflectance and atmospheric parameters. The results show the average difference of radiometric calibration coefficients between calculated results and the official coefficients in each year of ZY3-MUX sensor are 4.6%, 4.6%, 0.64%, 6.28%, respectively. The long-term stability of the radiometric calibration using the data of four years shows a good consistent, and the average difference is 0.92%, 0.64%, 0.50% and 0.65% for ZY3-MUX sensor, respectively. In addition, uncertainty analysis shows that the overall uncertainty for ZY3-MUX radiometric calibration is 4.42%, 4.44%, 4.66% and 3.92%, which also confirms the credibility for radiation quality of Baotou sandy site.

This paper describes an SNO (Simultaneous Nadir Overpass) method based on observation elements matching for radiometric cross-calibration of China HJ-1B and MODIS thermal infrared channels. Firstly, a DTC (Diurnal Temperature Cycle) model with four parameters and ECMWF data are introduced for time matching. Then a BRDF model updated to the TIR domain is built for angle matching. Combining matching coefficients with the spectral matching factor, the TOA radiance of MODIS B31 can be converted into TOA radiance of HJ-1B. Finally, the radiometric calibration results using the images of Qinghai Lake show that the proposed method is effective. Compared with the strict SNO method, the accuracy is improved by 0.73K.

In this study, the ozone column retrieval algorithm is described using the ultraviolet solar radiation from space. The algorithm is based on differential optical absorption spectroscopy (DOAS) technique. Firstly, the differential slant column densities (SCD) of trace gases are retrieved. Secondly, SCDs are subsequently converted to vertical column densities (VCD) by radiative transfer model. Then, the ozone column is retrieved by this algorithm and the sample results show a good correlation with R2of 0.91 and RMSE of 0.89 mg/m2between retrieved and true values. It also can be shown the potential of the algorithm on further atmospheric molecule retrieval in hyperspectral quantitative remote sensing.

This paper describes a four-component parameterized directional thermal radiance model for row crops, which consists of the thermal radiance of the sunlit/shaded soil and sunlit/shaded leaf, multiple scattering effects of canopy and sensor field of view. The light transmission process of the row crops canopy has been full depicted and the accuracy and sensitivity of the proposed model are discussed in detail. Finally, compared with the FRA97 and FovMod models, the results show that the root mean square error(RMSE) is 0.18K and 0.36K, respectively.

When using field calibration site to perform on-orbit radiometric calibration for a space-borne remote sensor, the observed signal of the sensor may contain energy from adjacent pixels, due to existence of the earth atmosphere and sensor viewing characteristics. Hence the accuracy of radiometric calibration will be decreased to some extent. When calibration of high-resolution sensor is concerned, the non-uniformity of the ground target will be relatively more obvious. In addition, if the brightness contrast of neighboring targets is not small, the impact of adjacency effect will be more outstanding. How to quantitatively analyze and eliminate the influence of this kind of adjacency effect, becomes an actual demand to reduce the uncertainty of on-orbit radiometric calibration. Aiming at the adjacent effect caused by atmospheric multiple scattering, this paper analyzed radiation transfer mechanism first, then constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data, developed an adjacency effect correction method used in on-orbit vicarious calibration, which considers background reflectance spectral information. Test on Sentinel-2A imagery indicates that the proposed correction method can effectively alleviate the influence of adjacency effect in vicarious calibration.

In this letter, a hyperspectral light detection and ranging (HSL) with 10-nm spectral resolution was designed and tested using a supercontinuum laser source. The major difference between the prototyped HSL and similar instruments was that a liquid crystal tunable filter (LCTF) was installed before the avalanche photodiode detector and utilized as a spectroscopic device. The design allowed continuous wavelength selection of the backscattered echoes in the time dimension. Moreover, for general accuracy evaluation of range measurement and spectral measurement, laboratory experiments for vegetation red edge detection were performed using the prototyped HSL to assess its feasibility on agriculture application. Yellow and green leaves from aloe and dracaena plants were measured by the LCTF-HSL for detecting the corresponding “red edge” position. Spectral profiles measured by an SVC-HR-1024 spectrometer which is designed by SVC company were used as a reference to evaluate the measurements of HSL. The comparison results showed that the red edge positions extracted from the two individual measurements were similar, thus indicating that the LCTF-based high-resolution HSL was effective for this application.

Radiometric characteristics monitoring of optical remote sensing data is a very essential step that enables further quantitative study and application of the data. In this paper, long term vicarious radiometric calibration using ground reflected radiance-based approach was introduced in this study. A case study of Sentinel 2A multispectral imager (MSI) vicarious radiometric characteristics monitoring based on our proposed approach with an automatic observation system was conducted over a large scale desert at the Baotou calibration site in Inner Mongolia, China. And, twelve clear Sentinel 2A MSI scenes as well as ground measurements were successfully acquired during the year of 2017. Top of Atmospheric (TOA) radiance and reflectance were predicted with introduced approach from ground reflected radiance and atmospheric data. The long term radiometric calibration results suggests that the Sentinel 2A MSI display a stable radiometric performance over the calibration period. Vicarious and onboard radiometric calibration results were also cross compared with average relative error about 5%. Uncertainty analysis also show that the TOA radiance overall uncertainty is less than 3.5% due to the atmospheric characteristics, surface characteristics, and the calibration model uncertainties sources.

Hyperspectral LiDAR (HSL) possesses the advantages of the LiDAR and the hyperspectral detection, and detects ranging and spectrum information synchronously, by one HSL system. The data fusion is also avoided. At present, the spectrum range of reported HSLs usually covers only 500 nm-1000 nm (from visual (VIS) to near infrared (NIR) band). However, there is requirement to extend the spectrum range to short wave infrared (SWIR) band, which often contains more useful spectral information. In this paper, a HSL covering the spectrum from VIS to SWIR is reported. In the HSL, the echoes are divided into two sections and are detected by the different optoelectronic devices, of which the spectral response ranges are respectively compatible to the corresponding echoes. The HSL detection experiment in the laboratory was carried out. The waveforms of the echoes were analyzed, and the spectra of different targets were measured by the HSL. The experiment results demonstrate the capability of the prototyped HSL that obtaining the ranging information and the spectrum information of the targets in VIS-SWIR bands synchronously.

In this study, the automatic reflectance-based method is used to vicarious radiometrically calibrate the satellite optical sensors using the desert target located in the Baotou site in China. The ground reflected radiance of the desert target were collected automatically using an automatic observation system. The reflectance of the desert target was calculated with the radiance collected with the automatic observation system and the total irradiance simulated from MODTRAN code based on the atmospheric parameters. Then, the TOA radiance can be predicted with MODTRAN code based on the calculated desert reflectance. The automatic reflectance-based approach was applied to the Landsat 8/OLI sensors, and the TOA radiances calibrated by our method were also compared with the observed TOA radiance calibrated with on-board calibrator. Preliminary results show a good consistent and the mean relative difference of the multispectral channels is less than 5%. Uncertainty analysis also show that the TOA radiance overall uncertainty is less than 4% due to the source including the atmospheric characteristics, surface characteristics, and the selected calibration model.

This paper addressed the retrieval of land surface temperature (LST) from combined mid-infrared and thermal infrared data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP). To efficiently remove the effect of the direct solar radiance, a relationship between direct solar radiance and water vapor content, view zenith angle and solar zenith angle is proposed to improve the retrieve accuracy. Then, a split-window algorithm from combined mid-infrared and thermal infrared data is used to correct for the atmospheric effects and retrieve the LST with the aid of emissivity provide by VIIRS product. Finally, comparison of the standard VIIRS LST product and the retrieved LST from the proposed algorithm, a good agreement was shown. Analysis indicated the root mean square error (RMSE) of the LST over these land cover types is 2.04K for desert and 1.84K for vegetation, respectively.

The characterization and understanding of microwave remote sensing image quality is essential to the monitoring performance of sensors and the proper usage of the acquired image. Point targets (e.g., passive corner reflectors and active transponders) have been widely used for microwave image resolution analysis. However, the analysis results based on point targets do not include the effects of speckle and thermal noise that are rare for point targets but common for distributed targets. Since distributed targets are common in remote sensing imaging scenes and their distinguishability is of interest in practice, the concept of an optical bar pattern target was extended to the microwave band, and a microwave bar pattern target was designed and permanently built at the National Calibration and Validation Site for High-Resolution Remote Sensors. In this letter, the main design idea is first introduced. Different backscatter coefficients of the bars were achieved for different surface roughness made by black rough gravel and a white smooth concrete plate, where the gravel size was designed per the Rayleigh roughness criterion. The experimental results using C-band airborne SAR and X-band KOMPSAT-5 SAR images are presented. A quantitative analysis shows that this target could roughly evaluate the image resolution of high-resolution microwave imaging sensor and would be a good complement to point targets.

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact, this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis (snail) is the unique intermediate host of schistosoma, so monitoring and controlling of the number of snail is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to snail breeding and reproduction, then can also provide the efficient information to determine the location, area, and spread tendency of snail. But the complex relationship between snail and various environmental factors limit its development. To solve above problem, in this study, the fuzzy information theory was employed to analyze the relationship between snail density and environmental factors. A model for predicting snail distribution and density was developed and validated with field data of Dongting Lake. The validation results demonstrated the success of the developed model in predicting the distribution of Oncomelania hupensis.

The assessment of Synthetic Aperture Radar (SAR) image resolution is essential to characterize and improve sensor performance, and to make better application of the acquired SAR data. Trihedral corner reflectors and active transponders have been widely used as standard point targets for SAR image spatial resolution assessment. However, these point targets have limitations in straight-forward result reveal and tolerance in deployment and processing errors. In light of the bar-pattern target which widely used for optical image resolution assessment, and to assess the image resolution of the SAR sensors operating at different frequency, different platform (airborne and spaceborne), a permanent bar-pattern target was designed and realized by black gravel and greyish white concrete bars. Gravel size, bar direction and width were carefully calculated according to the requirement of long-term operation. The effectiveness of the target was preliminarily validated by C-band airborne SAR, X-band spaceborne SAR data and optical image, and the result shows that the target is suitable for the spatial resolution assessment of both high-resolution SAR and optical sensors.

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Current TES methods were proposed and succeeded to apply for the retrieval of land surface temperature and emissivity for the materials with emissivity close to 1. This work addressed the performance of linear spectral emissivity constraint (LSEC) method proposed by wang et al. (2011) for the TES of hyperspectral TIR data for contrast samples (high- and low- emissivity materials). The simulated hyperspectral TIR data are used for analysis and generated with six MODTRAN standard atmospheric profiles by hyperspectral atmospheric radiative transfer model (4A/OP). The influence of initial emissivity estimation is considered in this paper. The results show that initial emissivity estimation has a great impact on the performance of LSEC. LSEC method performs a fairly good result when the initial emissivity is close to the true value, and the RMSEs of temperature and emissivity are smaller than 0.5K and 0.01 when initial emissivity is good. However, the performance is worst when the initial emissivity has a great deviation.

In this paper, great efforts are focused on the temperature and emissivity separation (TES) from hyperspectral thermal infrared data. However, instead of proposing new method, the performances of several published TES methods, including iterative spectrally smooth temperature emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI) and linear spectral emissivity constraint method (LSEC) are analyzed under different instrument characteristics, including the shifting of spectral and the broadening of the full-width half-maximum (FWHM), with the simulated data. The results shows that LSEC has the most robust and accurate performance. DRRI also has a good performance, but a channel selection procedure is required before the use of this method. ISSTES, ARTEMISS and SpSm are more sensitive to the instrument characteristics with some larger errors than other two methods.

In this paper, a methodology for maize LAI estimating is proposed by assimilating remotely sensed data into crop model based on temporal and spatial knowledge. Firstly, the spatial knowledge is extracted from MOD09A1 based on its multi-scale feature, and then the spatial knowledge is used for correcting the bias of inversion results. Secondly, the phenology information is extracted from MOD13A1 and used as prior temporal knowledge for building a cost function, and then, based on the cost function the sensitive parameters of WOFOST (WOrld FOod STudies) are calibrated. At last, the calibrated WOFOST model used as forecast operator and remote sensing inversion results used as observation operator, the Kalman Filter (KF) algorithm is used to realize the assimilation of MODIS data into crop model. The experiment results indicate that the methodology proposed in this paper is reasonable and accurate for estimating maize LAI.

For optical remote sensing images which are contaminated by white Gaussian noise, in general, uniform features indicate the same spectral characteristic. Uniform features will present the same or similar digital number (DN) value with a certain band in imaging. Therefore, the DNs of the uniform features are highly correlated [1]. When dividing an image into small blocks to estimate noise standard-deviations (SDs) and distributing SDs into a number of bins with equal width, within the minimum to the maximum SD, the statistical curve of numbers of SDs in bins theoretically meets Gaussian distribution [2]. Combining the two features, we develop a new method for SNR estimation. Results of tests indicate the new method performs better than other ones and overcome some disadvantages of some typical methods.

Remote sensing is an integrated Earth observation technology, and its standardization requires the multi-industry, multi-disciplinary and multi-field joint efforts and coordination. This paper discusses the management mechanism of remote sensing technology standardization, and reviews its current status in China. It's pointed that standardization work of remote sensing technology lags behind the development of remote sensing technology in the whole. Remote sensing technology standardization in China needs to be pushed urgently. Finally, some development suggestions of remote sensing technology standardization on the standard system, the international standardization and the publicizing and implementation of current standards are given.

Fractional vegetation cover (FVC) is a major biophysical parameter in earth surface system. In this paper, FVC is retrieved with a simple linear model between FVC and Normalized Difference Vegetation Index (NDVI). However, the parameters NDVI∞and NDVI0, corresponding to the values of NDVI for bare soil and full vegetation covered surface, used in the simple model are estimated with a vegetation phenology model using time series MODIS NDVI data. The results of the estimated FVC with our proposed method in the study area have been showed in the results section, which is compared with the FVC estimated with a single date MODIS NDVI data. Validation has also been proved that the retrieved FVC has a good agreement with the ground-measured truth FVC.

This work addressed the estimation of the directional reflectance in Middle Infra-Red (MIR) channel from the data acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard Chinese geostationary Meteorological satellite FengYun 2C (FY-2C). SVISSR/FY-2C sensor acquires image covering the whole disk with a temporal resolution of 30 minutes. The MIR directional reflectance retrieval procedure can be seen as follows. Firstly, the atmospheric profiles data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to correct atmospheric influence with the radiative transfer code (MODTRAN 4.0). Secondly, the bi-directional reflectance in SVISSR/FY-2C MIR channel 4 (3.8 micron) was estimated from the combined MIR and TIR channel with day-night SVISSR/FY-2C data. Finally, a BRDF model referred to as the RossThick-LiSparse-R model was used to estimate the directional reflectance in MIR channel from the time-series bi-directional reflectance data. The results have been demonstrated that the method can be applied well to estimate the directional reflectance in MIR channel of SVISSR/FY-2C sensor.

In this paper, an improved method is proposed to simultaneously retrieve land surface temperature (LST), emissivity (LSE) and atmospheric profiles. This method employed the linear spectral emissivity constraint to efficiently reduce the number of retrieved variables. The proposed method was validated with some simulations. The initial guesses were derived from a neural network model. This method could greatly improve the accuracies of LST, LSE and atmospheric profiles. The RMSE of LST was decreased from 5.12 K (the initial guesses) to 1.59 K (the physical retrieved). The retrieved emissivity spectrum was in good agreement with the actual spectrum. An improvement of 1K in the tropospheric temperature was also been found. Those results showed that the proposed method is capable of improving the retrieval accuracies of land surface and atmospheric parameters with the remotely sensed thermal infrared data.

In high resolution remote sensing images, urban buildings always have characteristics of complex structures and are vulnerable to background interference. For the purpose of detecting and mapping urban buildings automatically in that circumstance, a novel method is proposed in this paper. Firstly, the Conditional Random Field (CRF) is introduced to fuse multiple kinds of features to get the areas objects existing, then we propose a Hierarchical Object Process Model (HOPM), which is used to access to the location of objects as well as accurate depictions of their outline, and finally the corner detection method is utilized to delineate the vector shapes of objects. Competitive results for multiform and complicated urban buildings demonstrate the precision and robustness of the proposed method.

Microwave staring correlation (MSC) imaging is a new type of active high-resolution microwave imaging technique. Image quality assessment is of vital for developing and monitoring any remote imaging system. This paper presented the image quality analysis for this imaging technique by theoretical analysis and simulation. MSC imaging method was introduced firstly. Then, image properties were analyzed and compared with SAR image. Speckle noise effect and side lobe of point target effect, which are intrinsic properties of SAR image, do not exist in MSC image. The quality metrics of MSC image were presented. IRW-Staring time ratio was proposed to describe the image property that image resolution improves with the number of received signals used in the image reconstruction procedure. Finally, the effects of different system parameters on the image quality were investigated via simulation experiments. The analysis results could be helpful for future imaging algorithm development and system design.

This paper presents a weighted clustering algorithm based on the physical attraction model, which improves the physical attraction model by assigning a different weight to the position points on a GPS trace for a fast convergence according to their velocity and directional changes. The physical attraction model pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. Assuming a vehicle with a high velocity has a little derivation to the road it runs on, we assign a high weight to its trace in the physical attraction model so that the clustering progress converges rapidly and closely to road. Within the clustering process, an angle-threshold based smoothing filter is appended to keep the consistency between the changes of those points on the adjusted trace at each loop. This algorithm was demonstrated to enable to effectively clarify those vehicle GPS traces on the same road. In comparison of previous work, it also shows an improved quality grouping GPS traces near road-crossing area by embedding the smoothing filter in clustering process.