Face-swapping technologies have rapidly emerged as a mainstream AI service across entertainment, social media, and virtual platforms. While current face-swapping methods offer highly realistic results, they also introduce significant privacy risks, as most current approaches require clear target faces, exposing users’ identities. Moreover, the absence of built-in authorization and forensic mechanisms renders these systems incapable of tracing or verifying manipulated content, raising critical issues over accountability and potential misuse. To address these challenges, we propose a privacy-preserving and forensics-enabled face-swapping framework that simultaneously safeguards user identity and enables robust post-hoc face recovery. Instead of relying on visible target faces, our method operates on non-facial target images, fundamentally preventing identity exposure at the source. To ensure provenance traceability, we embed the target face’s features into non-facial regions of the generated image via an imperceptible and reversible encoding scheme. To further enhance robustness, we introduce a mask-distortion simulation layer that bridges pre-/post-swap mask discrepancies and stabilizes face recovery under perturbations. Extensive experiments demonstrate that our method produces realistic face-swapped images without revealing the original identity, while enabling high-fidelity recovery under various adversarial conditions—validating its effectiveness in both privacy protection and forensic traceability.

We propose a novel initialization and online spatial-temporal calibration method for visual-inertial odometry (VIO), which decouples rotation and translation estimation to achieve higher accuracy and better robustness. Existing initialization methods suffer from limited accuracy or robustness (e.g., in scenarios with small translational motion) and rarely integrate simultaneous spatial-temporal calibration during initialization, despite its considerable practical value. Our proposed method leverages rotation-translation decoupling constraints to enable simultaneous estimation of gyroscope bias, extrinsic rotation, and camera-IMU time offset-even under pure rotational motion. Moreover, we are the first to conduct observability analysis on rotational constraints in rotation-translation decoupling methods, experimentally identifying the unobservable state-space directions under three degenerate motions within our approach. We also perform extensive experiments to delineate practical parameter solution boundaries for our method, with both efforts substantially enhancing the overall practical applicability of decoupling-based methods. Extensive experiments on simulated and real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy and robustness while maintaining computational efficiency. Furthermore, experiments verify that it significantly improves convergence in VIO systems.

The 3rd Generation Partnership Project (3GPP) 5G Non-Terrestrial Networks (NTN) adopt Orthogonal Frequency Division Multiplexing (OFDM) to enable integrated space-ground networks via Low Earth Orbit (LEO) satellite global connectivity. However, the rapid movement of LEO satellites induces a significant non-uniform Doppler shift across subcarriers, resulting in the signal bandwidth changes that leads to sampling point offsets and severely impacting demodulation performance. Traditional Doppler compensation algorithms focus mainly on addressing the uniform Carrier Frequency Offset (CFO) caused by crystal oscillation. In LEO satellite broadband communication systems, the Sampling Frequency Offset (SFO), caused by the non-uniform Doppler frequency shift, can be tens of times greater than the CFO, leading to non-negligible phase rotation, inter-carrier interference (ICI), and inter-symbol interference (ISI). Consequently, a low-complexity algorithm is required to address the fast time-varying SFO — one of the core challenges in these systems. In this paper, we derive a closed-form expression for the signal distortion and propose a model-driven compensation method that leverages the predictability of satellite trajectories. The proposed method effectively removes phase rotation and mitigates ICI and ISI through fast Fourier transform (FFT) window adjustment and phase rotation compensation. The accuracy of the model is validated through extensive simulations and a real satellite communication trial. Results demonstrate that the proposed method supports higher-order modulations, leading to an average spectral efficiency improvement of approximately 50%. This pioneering research promises to ensure robust performance in dynamic LEO satellite environments.

This paper proposes a novel deep learning-based framework for hyperspectral band selection, named Cross-Scene Self-Representative Network (CSSRnet). The proposed method leverages the rich labels of the source domain (SD) to guide the band selection in the target domain (TD). To our knowledge, CSSRnet is the first deep learning-based solution for cross-scene hyperspectral band selection. First, the CSSRnet employs contextual attention mechanism to capture the latent features of SD and TD. It combines the self-attention mechanism with convolutional operations to capture static and dynamic contextual information. Then, the self-representative layer provides the self-representative coefficient of SD and TD. Subsequently, the maximum mean difference is utilized to align the self-representative coefficients of both SD and TD. To enhance the representativeness and precision of these coefficients, we introduce different tasks for the SD and TD branches. Finally, a suitable band subset is selected based on a ranking method that evaluates each band’s importance by considering its self-representative coefficient matrix. Experiments are carried out to assess the efficacy of CSSRnet. These experiments focus on evaluating classification accuracy across various cross-scene datasets, the utility of cross-scene concepts, and the practical application in coastal wetland. Experimental results confirm the effectiveness of CSSRnet.

While fusion of hyperspectral images (HSIs) with low spatial resolution and multispectral images (MSIs) with high spatial resolution has achieved significant success, high-quality fusion between noisy images has always been challenging. In this article, we propose a domain transform model driven by deep learning for anti-noise hyperspectral and multispectral image fusion (DTAFN). This marks the first time that wavelet decomposition theory is combined with deep learning for noise reduction in hyperspectral and MSI fusion. DTAFN initially decomposes hyperspectral and MSIs into frequency components and constructs a novel feature interaction fusion module (FIFM). This module, while using MSIs to guide the removal of noise from HSIs, also achieves the fusion of spatial and spectral information. Furthermore, it maps the fused features to a lower dimensional subspace to enhance computational efficiency. Additionally, we introduce a spatial-spectral self-attention mechanism to optimize the reconstructed frequency components using the subspace features. In the end, the wavelet inverse transform is used to reconstruct the clean fused image. It is worth noting that the extraction of the subspace is considered a process of nonlinear low-rank component extraction, which, to a certain extent, suppresses noise signals. Numerous experiments of mixed noise image fusion are carried out, and the experimental results show that DTAFN can obtain high-quality fusion results, is robust, and superior to the state-of-the-art methods.

Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendation (MDR), which aims to jointly improve recommendations on all domains and easily scales to thousands of domains, has attracted increasing attention from practitioners and researchers. Existing MDR methods usually employ a shared structure and several specific components to respectively leverage reusable features and domain-specific information. However, data distribution differs across domains, making it challenging to develop a general model that can be applied to all circumstances. Additionally, during training, shared parameters often suffer from domain conflict while specific parameters are inclined to overfitting on data sparsity domains. In this paper, we first present a scalable MDR platform served in Taobao that enables to provide services for thousands of domains without specialists involved. To address the problems of MDR methods, we propose a novel model agnostic learning framework, namely MAMDR, for the multi-domain recommendation. Specifically, we first propose a Domain Negotiation (DN) strategy to alleviate the conflict between domains. Then, we develop a Domain Regularization (DR) to improve the generalizability of specific parameters by learning from other domains. We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation. Finally, we present a large-scale implementation of MAMDR in the Taobao application and construct various public MDR benchmark datasets which can be used for following studies. Extensive experiments on both benchmark datasets and industry datasets demonstrate the effectiveness and generalizability of MAMDR.

The space geodetic technologies (e.g., Global Navigation Satellite System (GNSS) and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on (GFO)) provide effective tools to infer terrestrial water storage (TWS) change, which is an important indicator of the hydrological cycle and climate change. This study investigated the optimization approaches of the Slepian basis function (SBF) method in recovering TWS changes in the Southwest River Basin of China (SWRB) with sparse data and conducted a comprehensive analysis of TWS changes in SWRB using GNSS and GRACE/GFO. The results show that using the Shannon number criterion and even-distributed GNSS observations can obtain reliable TWS changes based on the SBF method. The GNSS-inverted TWS changes generally maintain good consistency with GRACE/GFO and hydrological model results in SWRB, but the ground-based daily GNSS estimates present stronger signal amplitudes and more high-frequency information. In the upper SWRB, the GRACE/GFO well reveals the continuous water loss (-8.19±1.76 mm/yr) induced by human activity, while the GNSS does not reveal the long-term linear changes of TWS, because it is difficult to separate them from the GNSS vertical displacement time series. In the lower SWRB, both GNSS and GRACE/GFO can reveal the increasing and decreasing trends of TWS changes within the study period, and precipitation is the dominant driving factor that causes the secular changes in TWS. Additionally, the ENSO generally presents relatively stronger effects on TWS changes than PDO in SWRB. Our results contribute to understanding the regional hydrological cycle and managing the water resources in SWRB using geodetic data such as GNSS and GRACE/GFO.

The great potential of NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) to retrieve sea ice heights has been demonstrated. However, the presence of a significant number of noise photons in the ICESat-2 data makes accurate monitoring of sea ice changes challenging. This paper proposes an adaptive clustering and kernel density estimation-based (AC-KDE) method for estimating sea ice heights in ICESat-2 photon clouds. First, the adaptive clustering method effectively detects sea ice signal photons. The method’s input parameters are determined based on the ATLAS parameters and the LiDAR transmission equation. Then, the adaptive-count signal photon aggregates are used to estimate sea ice heights, and a variable along-track resolution is obtained using the kernel density estimation method. The AC-KDE method is applied to the MABEL and ICESat-2 data, and we compare it with other denoising algorithms, including the HBM, DBSCAN, OPTICS, UMD_RDA, DDM, and ILSM algorithms. The results indicate that the proposed method outperforms these algorithms in extracting signal photons with higher accuracy scores and F-scores, which are 0.97 & 0.97, 0.92 & 0.90, and 0.89 & 0.72 under high-medium-low signal-to-noise ratio conditions, respectively. Additionally, the retrieved sea ice heights are compared with the ATL07 heights. The AC-KDE heights show a significant correlation with coincident ATM heights, and have a lower RMSE value (0.066 m) compared to ATL07 heights (0.104 m). The AC-KDE method also demonstrates a vertical height precision of 0.01 m over flat leads. The proposed method can effectively extract signal photons and accurately estimate sea ice heights in polar regions.

Image registration aims to eliminate the geometric deviation between multi-source data with the same range, and to promote the collaborative application of data. In recent years, spaceborne hyperspectral (HS) and multispectral (MS) data have been widely used in Earth observation. However, the difference in the number of bands, spatial resolution, and spectral resolution puts forward higher requirements on the registration algorithm. The key to HS and MS image registration is to extract more common key points, weaken and eliminate the difference of radiation and spatial texture information to build superior descriptors, and achieve high-precision matching of key points. This paper introduces a new robust HS and MS registration method based on common deep feature subspaces. We first construct the common deep feature subspaces extraction network to extract consistent edge features and common subspace images of the image pair. Then, Harris algorithm is used to extract key points from consistent edge features between images, which reduces the impact of spatial resolution differences between images. Besides, the SIFT descriptor and subspace images are used to describe key points, which reduces the impact of radiation differences between images. Finally, Euclidean distance is used for the initial matching of key points, and the affine matrix is calculated after the outliers are eliminated, and image registration is performed. We perform experiments on spaceborne HS and MS datasets of different spatial resolutions and comparisons with state-of-the-art methods. Experimental results show that our method can obtain satisfactory registration results and is robust.

Due to sensor design limitations and the influence of weather factors, it is currently challenging to obtain remote sensing images with high temporal, spatial, and spectral resolution. Spatial-temporal-spectral fusion aims to integrate the temporal, spatial, and spectral information from multiple sources of remote sensing images to reconstruct a remote sensing image with high temporal, spatial, and spectral resolution. Existing methods typically require at least three types of data to achieve spatial-temporal-spectral fusion. However, acquiring remote sensing data observed at the same time poses significant difficulties. The major challenge lies in effectively utilizing hyperspectral images with low spatial and temporal resolution and multispectral images with high temporal and spatial resolution to reconstruct remote sensing images with high temporal, spatial, and spectral resolution. To address the aforementioned issues, we propose a novel unsupervised 3D tensor subspace decomposition network. Our method incorporates the theory of 3D tensor subspace decomposition, utilizing a 3D hyperspectral/multispectral tensor subspace extraction network to predict the hyperspectral tensor subspace features with low spatial resolution missing at other times (To better understand, the missing moment is defined as time 2). Subsequently, the 3D hyperspectral tensor subspace reconstruction network is employed along with the time 2 hyperspectral tensor subspace features with low spatial resolution and the time 2 multispectral image to reconstruct the time 2 hyperspectral image with high spatial resolution. In the experiment, we utilize three simulated datasets and two real datasets to evaluate the fusion performance of our proposed method. The results demonstrate that our method achieves high-quality fusion results and exhibits comparable performance, and has robustness and practicality.

In proteomics, data-independent acquisition (DIA) has been shown to provide less biased and more reproducible results than data-dependent acquisition. Recently, many researchers have developed a series of methods to identify peptides and proteins by using spectrum libraries for DIA data. However, spectrum libraries are not always available for novel organisms or microbial communities. To detect peptides and proteins without a spectrum library, we developed IDIA, a library-free method using DIA data to generate pseudo-spectra that can be searched using conventional sequence database searching software. IDIA integrates two isotopic trace detection strategies and employs B-spline and Gaussian filters to help extract high-quality pseudo-spectra from the complex DIA data. The experimental results on human and yeast data demonstrated that our approach remarkably produced more peptide and protein identifications than the two state-of-the-art library-free methods, i.e., DIA-Umpire and Group-DIA. IDIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/IDIA.

Global Navigation Satellite System interferometric reflectometry is an emerging remote sensing technique that can be used to measure a wide range of geophysical parameters. In this letter, a low-cost navigational GNSS chip and RHCP antenna were used to receive and process the interference GNSS signal in a winter wheat farmland. By simplifying the wheat crop as multi-layer equivalent mediums (EMs), the characteristics of GNSS SNR observations recorded by the instrument were analyzed. The confidence level of the Lomb-Scargle spectral analysis result was used to identify the peak frequencies of the GNSS SNR series. Based on the peak frequencies, the EM heights can be calculated. The height estimations were used to compare with thein situwheat height measurements. The results show that the estimated EM height is inversely proportional to thein situwheat height in the wheat stem extension stage, with a correlation coefficient of −0.9939; and the estimation is very close to thein situones in wheat heading and ripening stages, with a root-mean-square error of 5.8 cm when the wheat height ranges between 40 and 75 cm.

In the last 20 years, the reflected signal of Global Navigation Satellite System (GNSS) has been used for remotely sensing a series of geophysical parameters, resulting in two GNSS based remotely sensing techniques: GNSS reflectometry (GNSS-R) and GNSS interferometric reflectometry (GNSS-IR). In this letter, the refracted GNSS signal is first proposed to estimate near-surface soil moisture (SM). Amplitude of the refracted GNSS signal will attenuate when penetrated into soil due to refraction and propagation of the signal in the soil. Amplitude attenuation degree of the refracted signal is quantified as the amplitude ratio (AR) of the direct GNSS signal to the refracted signal. Two low-cost navigational GNSS chips and right-hand circularly polarized (RHCP) antennas are used to collect the refracted and direct GNSS signal in an experimental campaign, respectively. To simplify the modeling, the AR at elevation angle of 20° is used to develop the model to describe the relationship between SM, AR, and soil temperature (ST) in the letter; and the AR and ST observation can be converted into SM accurately with a 2nd-order polynomial. The modeled SMs are strongly correlated with the sensor-based ones with correlation coefficient of 0.947 and root-mean-square error (RMSE) of 0.013 cm3/cm3(or, 1.3%) when SM is between 0.272 and 0.489 cm3/cm3. The study also suggests that, based on the proposed method, the low-cost GNSS instrument can be treated as a new type of sensor monitoring SM in a cost-effective way.

Soil moisture is fundamental to land surface hydrology, affecting flooding, groundwater recharge, and evapotranspiration. In this paper, a low-cost GNSS receiver is used to estimate soil moisture the first time. A new soil moisture estimation method based on the receiver SNR data is proposed. The relationship between amplitude attenuation factor (AAF) of SNR and soil moisture is investigated by using in-situ observations at first. Then, the retrieval method of SNR AAF is proposed. GPS data collected over a month in Chongqing, China was used to test the proposed method. Based on in-situ SNR and soil moisture observations, 1stand 2ndregression functions converting AAF into soil moisture were established by least squares method. The preliminary results show that there is a good agreement between the insitu soil moisture and the estimated one by the proposed method, with the RMSE smaller than 0.012 when soil moisture is in the range of 0.35 to 0.45.

We present MMFashion, a comprehensive, flexible and user-friendly open-source visual fashion analysis toolbox based on PyTorch. This toolbox supports a wide spectrum of fashion analysis tasks, including Fashion Attribute Prediction, Fashion Recognition and Retrieval, Fashion Landmark Detection, Fashion Parsing and Segmentation and Fashion Compatibility and Recommendation. It covers almost all the mainstream tasks in fashion analysis community. MMFashion has several appealing properties. Firstly, MMFashion follows the principle of modular design. The framework is decomposed into different components so that it is easily extensible for diverse customized modules. In addition, detailed documentations, demo scripts and off-the-shelf models are available, which ease the burden of layman users to leverage the recent advances in deep learning-based fashion analysis. Our proposed MMFashion is currently the most complete platform for visual fashion analysis in deep learning era, with more functionalities to be added. This toolbox and the benchmark could serve the flourishing research community by providing a flexible toolkit to deploy existing models and develop new ideas and approaches. We welcome all contributions to this still-growing efforts towards open science: https://github.com/open-mmlab/mmfashion.

Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which is one of the most typical machine learning tasks in personalized advertising and recommender systems. Although developing hand-crafted interactions is effective for a small number of datasets, it generally requires laborious and tedious architecture engineering for extensive scenarios. In recent years, several neural architecture search (NAS) methods have been proposed for designing interactions automatically. However, existing methods only explore limited types and connections of operators for interaction generation, leading to low generalization ability. To address these problems, we propose a more general automated method for building powerful interactions named AutoPI. The main contributions of this paper are as follows: AutoPI adopts a more general search space in which the computational graph is generalized from existing network connections, and the interactive operators in the edges of the graph are extracted from representative hand-crafted works. It allows searching for various powerful feature interactions to produce higher AUC and lower Logloss in a wide variety of applications. Besides, AutoPI utilizes a gradient-based search strategy for exploration with a significantly low computational cost. Experimentally, we evaluate AutoPI on a diverse suite of benchmark datasets, demonstrating the generalizability and efficiency of AutoPI over hand-crafted architectures and state-of-the-art NAS algorithms.

Continuous and reliable monitoring of world-wide snowfall is important for study of climate change and water resource utilization. Both snow depth and snow water equivalent (SWE) are the measure of snowfall, but SWE is a more useful measure, which is defined as the product of snow depth and snow density. Global Navigation Satellite System reflectometry (GNSS-R) is a new remote sensing technology that can enable cost-effective global-scale and continuous monitoring of snowfall. This paper presents a new snow density estimation method based on GNSS-R by matching the envelope of the SNR amplitude with that of the modeled SNR amplitude. Field experimental data are used to evaluate the proposed model based snow density estimation method. The experimental results demonstrate that the RMS of the density estimation error is 0.02gcm-3.

Recursive neural network (RvNN) has been proved to be an effective and promising tool to learn sentence representations by explicitly exploiting the sentence structure. However, most existing work can only exploit simple tree structure, e.g., binary trees, or ignore the order of nodes, which yields suboptimal performance. In this paper, we proposed a novel neural network, namely TreeNet, to capture sentences structurally over the raw unconstrained constituency trees, where the number of child nodes can be arbitrary. In TreeNet, each node is learning from its left sibling and right child in a bottom-up left-to-right order, thus enabling the net to learn over any tree. Furthermore, multiple soft gates and a memory cell are employed in implementing the TreeNet to determine to what extent it should learn, remember and output, which proves to be a simple and efficient mechanism for semantic synthesis. Moreover, TreeNet significantly suppresses convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) with fewer parameters. It improves the classification accuracy by 2%-5% with 42% of the best CNN’s parameters or 94% of standard LSTM’s. Extensive experiments demonstrate TreeNet achieves the state-of-the-art performance on all four typical text classification tasks.

The aim of scene semantic segmentation is to label each pixel with a class which it belongs to in high level cognition. State-of-art works mainly adapt convolutional neural networks originally designed for image classification to make dense prediction. However the inner structure of scene itself and its stuff is more flexible and variable, which is distinct from the objects in image classification task. Therefore we propose a gate function based structure-aware convolution for deep neural networks with the ability of modeling inner variance in scene. The gate function is a RNN-based learnable function or a handcrafted one, which is applied to distinguish efficient activations from convolution area. It is proved that dilated convolution is a subclass of gate function. As shown in our experiments on scene datasets, the proposed convolution method efficiently improves the accuracy of current semantic segmentation systems by partly replacing original networks' convolution layers with ours.

Monitoring snow distribution area plays an important role in researching climate change and energy exchange process. Chinese small satellite constellation (abbreviated HJ constellation) is special for environment and disaster continuously monitoring or forecasting. By using HJ-1B CCD and infrared data or only using its infrared data, it can construct NDSI (Normalized Difference Snow Index) or MNDSI (Modified Normalized Difference Snow Index) respectively to detect snow area. However, obtaining the CCD and infrared images at the same time is impossible for the long time monitoring. Furthermore, snow area is mixed with vegetation, the snow index's value will be reduced, and snow area in these places could not be detected accurately. Therefore, this paper introduces an improved snow cover monitoring method based on MNDSI and priori information of vegetation to increase the detection accuracy of snow area by using HJ-1B infrared image. The proposed method has the higher precision than the compared method.

The study of flood detection is significant to human life and social economy. In this paper, a completely unsupervised flood detection approach is presented, which combines spatio-temporal context and histogram thresholding. A global thresholding algorithm can be used in most of the cases to distinguish flood from non-flood pixels, but it may not distinguish local grey-level changes when the method is unsupervised. In this work, we introduce a kind of local context information to improve the results. A statistical model is used to establish the spatial relationships between each pixel and its surrounding regions, then a confidence map is computed. If the context structure changes significantly, the pixel is then considered potentially abnormal. Experimental investigations performed on HJ-1B CCD data from Northeast China during large-scale flooding in August 2013 showed higher precision of the proposed approach.

This paper presents a fully integrated reconfigurable all-band RF transceiver for GPS/GLONASS/ Galileo/Beidou in 55-nm CMOS. The transceiver incorporates three low-IF receivers (RXs) and one direct up-conversion BPSK transmitter (TX), which can be configured to receive any two global navigation satellite system (GNSS) signals or switched to process the Chinese Beidou(I) signals. A switching module is integrated to provide the connectivity between different RF front-end and IF channel (IFC), which will effectively simplify the design complexity of the IFC and save power consumption, while the GNSS signals are received. A flexible frequency plan with two frequency synthesizers is utilized to satisfy different local oscillator requirements of the transceiver. An optimized automatic frequency calibration scheme using an error compensation logic enables fast and high-precision calibration process for optimum phase-locked loop operation. Several digitally assisted calibration modules are integrated to ensure that the chip performance only shows a weak process, voltage and temperature (PVT) dependence. While drawing about 21.5-30.2 mA per RX channel from a 1.2-V supply, the RXs achieve an image rejection ratio more than 49 dB after I/Q mismatch calibration, an automatic gain control range of 88 dB, and an input-referred 1 dB compression point of better than -25 dBm with a minimum noise figure of about 2 dB. The output power of the TX is about 5 dBm with about 6% error vector magnitude (EVM) and 30-mA current from a 1.2-V supply. The whole transceiver consumes a die area of 2.8 × 3 mm2.

Total variation has been used as a popular and effective image prior model in the regularization-based image processing fields. However, as the total variation model favors a piecewise constant solution, the processing result under high noise intensity in the flat regions of the image is often poor, and some “pseudo-edges” are produced. In this paper, we develop a regional spatially adaptive total variation (RSATV) model. Firstly, the spatial information is extracted based on each pixel, and then two filtering processes are respectively added to suppress the effect of “pseudo-edges”. After that, the spatial information weight is constructed and classified with k-means clustering, and the regularization strength in each region is controlled by the clustering center value. The experimental results, on both simulated and real datasets, show that the proposed approach can effectively reduce the “pseudo-edges” of the total variation regularization in the flat regions, and maintain the partial smoothness of the highresolution image. More importantly, compared with the traditional pixel-based spatial information adaptive approach, the proposed region-based spatial information adaptive total variation model can better avoid the effect of noise on the spatial information extraction, and maintains robustness with changes in the noise intensity in the super-resolution process.

The accurate geolocation (horizontal and vertical position) of a laser altimeter's surface returns (the spots from which the laser energy reflects on the earth's surface) is a critical issue in the scientific application of these data. The geolocation of laser surface return is computed from the laser-altimeter surface range observations along with the precise knowledge of spacecraft position, instrument tracking points, spacecraft attitude, laser pointing and observation times. These data have errors, and their pre-launch parameter values and models must either be verified or more likely corrections must be estimated once the instrument is on orbit. ICESat is no exception and pointing, ranging and timing corrections must be calibrated and validated post-launch. Towards this end, this article raise a design on calibration and validation of ICESat data, focusing on geodetic aspects of mission.