Nighttime light (NTL) remote sensing has emerged as a pivotal tool across diverse fields such as urban expansion, socioeconomic estimation, light pollution, and energy analysis. However, existing NTL datasets with different scales exhibit shortcomings including low spatial resolution, infrequent updates, and inadequate observation timing, hindering their suitability for large-scale fine-grained applications. To address these challenges, this study designs the NTL Spatiotemporal fusion (STF) strategy, which amalgamates the strengths of multi-source NTL data, including Spatiotemporal Super Resolution (STSR) and Local NTL Recovery (LNR). Leveraging data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Sustainable Development Goals Satellite-1 (SDGSAT-1), the study constructs the spatiotemporal fusion NTL (STF-NTL) dataset to analyze NTL trends in France. The research demonstrates that, compared to alternative super resolution (SR) methods, the STSR approach excels in texture structure and evaluation accuracy. The improved module in STSR achieves superior image quality by balancing image error and human visual perception. Furthermore, STF-NTL data following LNR exhibits enhanced matching accuracy with impervious surfaces compared to other NTL datasets, aligning more closely with economic development and urbanization trends. Application of the STF-NTL dataset in France reveals an increase in NTL area in 2022 relative to 2012, rectifying statistical disparities observed with VIIRS data. Analysis indicates that regional conflicts, energy crises, and policy shifts influenced fluctuations in monthly NTL across France in 2022. The period from March to September 2022 demonstrates a strong correlation between NTL changes and the electricity market, while post-September dynamics are predominantly shaped by energy-saving policies. The STF-NTL datasets can be produced on a global scale, offering enhanced accuracy in reflecting economic, urban, and societal conditions.

Nighttime light remote sensing is widely employed to monitor human activities and light pollution within nature reserves. Previous research has predominantly focused on light pollution within reserves, neglecting the surrounding light pollution intensity and quantification of ecological quality changes in and around protected areas. This study, centered on China’s inaugural national parks, utilizes nighttime light data and the Remote Sensing Ecological Index (RSEI) for a long-term assessment (2000-2022). Results reveal increased human activity and light pollution within the buffer zones but overall stable or improved ecological quality within most national park areas (>96%). Geodetector analysis explored factors influencing ecological quality changes, finding land use type, elevation, precipitation and their interactions most impactful. The establishment of reserves helped maintain ecosystem quality despite increased human pressures. This research provides an assessment of ecological impacts from recent conservation policies in China to support improved protected area planning and management.

The Gaofen-1 (GF-1) wide-field-view (WFV) sensor provides valuable Earth observation (EO) data, but its limited geometric accuracy hinders applications requiring precise geolocation. This study presents a novel and practical on-orbit geometric recalibration method specifically designed for satellite images lacking a rigorous sensor model. Our approach uses a rowwise rational polynomial coefficient (RPC) refinement model and leverages well-distributed ground control points (GCPs) within single rows of calibration images to estimate and correct charge-coupled device (CCD) distortions using thin plate spline (TPS) interpolation. Validation results demonstrate a significant improvement in geometric accuracy, achieving a circular error 90% (CE90) of approximately 0.4 pixels for GF-1 WFV, highlighting the method’s effectiveness and broad applicability in remote sensing.

Sharpening a single satellite image remains challenging due to low computational efficiency, complexity of multiparameters, unphysical modeling, and the potential for radiometric consistency loss. To address these issues, this article introduces a modulation transfer function (MTF)-based sharpening method that is fast, has a single tunable parameter, and effectively suppresses noise and over-enhancement. This article also proposes an automatic method for extracting edge objects with any angle for MTF calculation, without relying on ideal edge objects. The improved slanted-edge method is more robust against noise by incorporating the logistic function and employing the random sample consensus (RANSAC) algorithm to remove deflected edges. The new 2-D MTF estimation method provides precise and stable sharpening results. This article extends the proposed method to single image super-resolution (SISR) for satellite images. The proposed approach outperforms state-of-the-art SISR methods, including 11 deep learning-based methods, across three public datasets and raw images (water, city, and building) acquired from three satellites. The utmost correlation to the histogram of raw image proves the proposed method’s superiority in preserving radiometric information compared to other methods. In addition, the successful application of the one-time estimated 2-D MTF for raw satellite images over a year and its capability to improve edge sharpness uniformity across cameras within the sensor system further solidify the method’s universality and reliability. More comparison results and code are available athttps://github.com/RSingKK/Any-angle-MTF.

Forests are the most widely distributed terrestrial vegetation type and play a significant role in the global carbon cycle and ecological diversity. Accurate and timely forest detection provides essential data for forest management and development. Current forest-related products differ in definition, accuracy, and spatial consistency, making them difficult to use. Therefore, it is necessary to map forest cover under a unified framework. However, detecting forests on a large scale requires high-quality and representative samples, which can be challenging. This study proposes a weakly supervised forest classification framework (WSFCF) that uses noisy labels. The WSFCF is designed to address label generation, correction, and sample location optimization. We employ a spectral-spatial network to extract forest cover accurately for medium-resolution forest classification. The experimental results show that the proposed method outperforms the compared methods, achieving an accuracy of 91.76% OA and 88.28% F1 score on 110 GF-1 WFV images. This supports the subsequent extraction of national-scale forest cover and encourages the mapping of China’s forest cover using GF-1 WFV images. Moreover, the proposed method produces satisfactory outcomes for objects such as water, farmland, and built-up areas within the study area, demonstrating its effectiveness and potential for transferability.

Raw Night-Time-Light (NTL) images captured by the Glimmer Image for Urbanization (GIU) sensor of the SDGSAT-1 satellite face the problem of stripe noise and vignetting. This paper proposed a universal method for relative radiometric correction of NTL images captured by push-broom system. Firstly, NTL ground object pixels from stripe noise were masked by setting thresholds of digital number (DN) value, allowing for the calculation of stripe noise thresholds. Secondly, a new vignetting correction was proposed by using a novel "Night-Day" orbital images as calibration data. The calculated stripe noise thresholds and vignetting correction parameters can be applied to other raw orbital images. The results were found to be superior to existing methods. In addition, using the calculated correction parameters can solve the residual stripes existing in the official products. Finally, the results of relative radiometric correction on raw images from different places further demonstrate the credibility of the proposed method.

The challenge of performing relative radiometric correction on raw Night-Time-Light (NTL) images captured by the Glimmer Image for Urbanization (GIU) sensor of the SDGSAT-1 satellite is the presence of stripe noise and vignetting. To address this issue, this paper presents a universal method for relative radiometric correction of NTL images captured by the push-broom system. A new automated approach to NTL pixel identification based on Gray-level Co-occurrence Matrix (GLCM) was developed to mask NTL ground object pixels from stripe noise, allowing for the calculation of credible stripe noise thresholds. A novel calibration data called "Night-Day" orbital data was introduced for vignetting correction. The "Night-Day" orbital data features an abnormal transition zone that can be used to determine the vignetting correction parameters. The stripe noise thresholds and vignetting correction parameters can be applied to other raw orbital images. Experiments were conducted on raw images from different dates to verify the universality and robustness of the method, and the results were found to be superior to existing methods. A comparison was also made between the calibrated images and original official Level-1 products, with the results indicating that the correction parameters calculated by the proposed method resolve the defects in the original Level-1 products. The correction parameters have been accepted by the official and have been used to update the original GIU Level-1 products. Finally, the results of relative radiometric correction on raw images from around the world further demonstrate the universality and credibility of the correction parameters.

Applications of aerial imaging, especially based on unmanned aerial vehicles (UAVs) platform, rapidly explode in recent years. Meanwhile, vision-based sensing, e.g., detection and recognition, for UAVs becomes increasingly important. Objects in aerial images are usually of tiny size, hence occupying a limited area. Terminology speaking, the images are very sparse in spatial. However, existing work in aerial object detection commonly ignores this point. Conversely, we explore the availability of such a property in improving the detection performance of aerial images. Specifically, we propose a general method, train in dense and test in sparse (TDTS), to exploit sparsity in aerial object detection: 1) in the training stage, the possible positions of object are learned by training a fully convolutional network (called prophet head) and 2) in the testing stage, prophet head identifies the possible object locations to reduce redundant computation in classification and box prediction head by sparse convolution. By extensive experiments on the VisDrone2019-Det data set, we find that the sparsity can not only help to speed up inference but also to improve accuracy. Thus, we argue that the sparsity deserves more attention.

The impervious surface (IS) cover and its dynamics are key parameters in research about urban and ecology. This letter proposed an automatic framework to map the IS growth end-to-end based on the temporal deep learning (DL) model and long time-series Landsat imagery. First, the training and validating datasets were auto-generated by a joint strategy. Then, a DL network was designed, and the IS growth was predicted in temporal windows. Finally, the results from multi-temporal windows are combined to generate the IS growth map. The data around the core of Beijing, China, is tested, and the result shows that the proposed method could: 1) efficiently model the IS growth; 2) map IS growth with less salt-and-pepper noise and false alarm compared to existing products; and 3) be extended to future data easily.

The relative radiometric correction for Level-0 images captured by spaceborne push-broom imaging system faces the problems of vignetting, chromatic aberration, brightness saturation difference, misalignment, and so on. This article proposed a general relative radiometric correction method, which was applied to Gaofen series satellite Level-0 images. In the course of vignetting calibration, the proposed method based on the gray-level co-occurrence matrix (GLCM) did not use side-slither data and the DNMAXtruncation method can solve brightness saturation difference. During chromatic aberration calibration, the subpixel-based phase correlation algorithm was first used to register adjacent CCDs, and then, the proposed global optimization method was adapted to calibrate multiple CCDs. The fixed calibration parameters for vignetting and chromatic aberration calculated by ridge regression and Newton’s method can be directly applied to correct other orbital Level-0 images. To verify the robustness and universality, Level-0 images of GF-1B, GF-1C, GF-1D, and GF-2 satellites were chosen for experiments, and the results were better than the existing methods. In addition, some official Level-1 products of GF-1B and GF-1C, covering particularly dark or bright surfaces (e.g., snow, sea, and cloud), were used to compare with the calibrated images of the proposed method. Results showed that relative radiometric correction by applying the independently estimated calibration parameters in this work achieved satisfactory results without the defects existing in official Level-1 products. Finally, results of relative radiometric correction of 30 orbital Level-0 images around the world further strengthened the conclusion that the estimated calibration parameters can be reused in other regions or seasons.

Due to the limitation of technology and budget, it is often difficult for sensors of a single remote sensing satellite to have both high temporal resolution and high spatial (HTHS) resolution at the same time. In this paper, we proposed a new Multi-level Feature Fusion with Generative Adversarial Network (MLFF-GAN) for generating fusion HTHS images. MLFF-GAN mainly uses U-net-like architecture and its generator is composed of three stages: feature extraction, feature fusion, and image reconstruction. In feature extraction and reconstruction stage, the generator employs the encoding and decoding structure to extract three groups of multi-level features, which can cope with the huge difference of resolution between high-resolution images and low-resolution images. In the feature fusion stage, Adaptive Instance Normalization (AdaIN) block is designed to learn the global distribution relationship between multi-temporal images, and an attention module (AM) is used to learn the local information weights for the change of small areas. The proposed MLFF-GAN was tested on two Landsat and MODIS datasets. Some state-of-the-art algorithms are comprehensively compared with MLFF-GAN. We also carried on the ablation experiment to test the effectiveness of different sub-module in MLFF-GAN. The experiment results and ablation analysis show the better performances of the proposed method when compared with other methods. The code is available at https://github.com/songbingze/MLFF-GAN.

Aerial remote sensing image products are divided into 4 levels. The quality of Level-0 products determines the quality of other level products. High resolution optical satellite systems use optical focal plane assemblies to enhance the image width using push-broom imaging. Level-0 images obtained by this system will exist vignetting and chromatic aberration in the overlapped regions, affecting the use of image products. Currently, the main solutions include laboratory radiometric calibration, on-orbit relative radiometric calibration, statistical methods and histogram based on side-slither data. In order to solve defects of the existing methods, this paper proposed a general radiometric calibration method for vignetting and chromatic aberration of multiple CCDs, which outperformed existing ones. In addition, defective GF-1C image product delivered by the official agency (China Centre for Resources Satellite Data and Application) is selected for comparison, and the chromatic aberration and supersaturation existing in the official product can be solved by directly applying the proposed method with the correction parameters estimated from independent calibration dataset.

Building detection from high-resolution remote sensing images is one of the tasks of remote sensing information extraction. Due to the particularity of the radiation quantization value and spatial resolution of Gaofen-2 satellite, the shape of buildings cannot be clearly represented in many scenes, so it's difficult to realize building detection in complex scenes. This paper made improvements based on the instance segmentation network CenterMask. Because the shapes of the building is irregular, traditional convolution cannot express spatial deformation well. So we used deformable convolution to replace the traditional convolution. In the attention mechanism, channel-dimensional attention can highlight useful channels and weaken redundant channels. The spatial attention can capture long-distance dependence through global context information. We added a channel attention mechanism to the segmentation branch. The feature map passes through the channel attention mechanism and the spatial attention mechanism successively. Experimental results show that the improved model can achieve good results in building detection tasks under complex scenes.

As the direct geo-locating accuracy of spaceborne optical images is limited by the uncertainty of the exterior orientation parameters, precise ground control points (GCPs), which are difficult or expensive to obtain, are commonly required to improve the geometric accuracy in practical applications. In this article, we propose a novel block adjustment (BA) method to make use of the GCPs automatically collected from reference images of coarse resolution (Landsat-8 or Sentinel-2), which are publicly available. Different from the conventional BA methods, the proposed one treats the GCPs of low accuracy as relaxed constraints instead of directly minimizing the error between the geometric models and GCPs, and only guarantees that the GCPs are satisfied by the geometric models with a prior accuracy. In addition, an automated method is introduced to estimate the proper GCP accuracy for the proposed BA. The experimental results of three testing sites in China using three different types of spaceborne images, i.e., Gaofen-1 (GF-1) panchromatic (PAN), ZY-3 nadir (NAD), and SPOT-5 high resolution geometric (HRG) whose spatial resolutions are around 2 m, show that the accuracy of 1-2 pixel can be achieved for these high-resolution images when only coarse reference images (spatial resolution of 15 and 10 m) were used as ground control. The results also show that the inaccurate GCPs are not likely to undermine the geometric consistency of images in the proposed BA, and BA with relaxed constraints can even achieve better tie points (TPs) accuracy than BA without ground control. This article provides a practical way to utilize inaccurate ground control and balance the tradeoff between GCPs and TPs.

Image registration is a critical process for the various applications in the remote sensing community, and its accuracy greatly affects the results of the subsequent applications. Image registration based on phase correlation has been widely concerned due to its robustness to gray differences and efficiency. After calculating the normalized cross-relation matrix Q, the most commonly used approach is fitting the 2-D phase plane that passes through the origin, but it needs to remove contaminated spectrum carefully and the corresponding parameters are empirical. In fact, the phase correlation matrix is rank one for a noise-free translation model. This property simplifies the matching problem to finding the best rank-one approximation of the normalized cross-relation matrix. We develop a novel algorithm that performs the rank-one matrix factorization on the phase correlation matrix by assuming its noise as mixture of Gaussian (MoG) distributions. The MoG model is a general approximator for any continuous distribution, and hence is able to model a wide range of noise distribution. The parameters of the MoG model can be evaluated under the framework of maximum likelihood estimation by using an expectation-maximization method, and the subspace is calculated with standard methods. The advantages of the algorithm, high accuracy, and robustness to aliasing, noise, gray difference, and occlusions are illustrated by a series of simulated and real-image experiments.

A rational function model (RFM), which consists of 80 rational polynomial coefficients (RPCs), has been widely used to take the place of rigorous sensor models in photogrammetry and remote sensing. However, it is difficult to solve the RPCs because of the requirement for numerous observation data [ground control points (GCPs)] in a terrain-dependent case and the strong correlation between the coefficients (ill-poseness). Regularization methods are usually applied to cope with the correlations between the coefficients, but only ℓ2-norm regularization is used by the existing approaches (e.g., ridge estimation and Levenberg-Marquardt method). The ℓ2-norm regularization can make an ill-posed problem well-posed but does not reduce the requirement for observation data. This paper presents a novel approach to estimate RPCs using ℓ1-norm-regularized least squares (L1LS) , which provides stable results not only in a terrain-dependent case but also in a terrain-independent case. On one hand, by means of L1LS, the terrain-dependent RFM becomes practical as reliable RPCs can be obtained by using much less than 40 or 39 (if the first denominators are equal to 1) GCPs, without knowing the orientation parameters of the sensor. On the other hand, the proposed method can be applied to directly refine the terrain-independent RPCs with additional GCPs: when a single or several GCPs are used, direct refinement performs similarly to bias compensation in image space; when more GCPs are available, the direct refinement can achieve comparable accuracy of the rigorous sensor model (better than conventional bias compensation in image space) .

Dynamic program execution monitors allow programmers to observe and verify an application while it is running. Instrumentation-based dynamic program monitors often incur significant performance overhead due to instrumentation. Special hardware supports have been proposed to reduce this overhead. However, these supports mostly target specific monitoring requirements and thus have limited applicability. Recently, with multi-core processors becoming mainstream, executing the monitored program and the monitor simultaneously on separate cores has emerged as an attractive option. However, communication between the two often becomes the new performance bottleneck due to large amounts of information forwarded to the monitor. In this paper, we present compiler techniques that aim to minimize the communication overhead. Our proposal is based on the observations that a monitor only requires specific information from the monitored programs and some information can be easily computed by the monitor from data that have already been communicated. We developed a code generator and optimization techniques to decide the set of data items to forward and the set to compute, so that the total execution time of the monitor is minimized. Our compiler can optimize a variety of monitors with diverse monitoring requirements, taking as input the control flow graph of the monitored program and the set of data that needs verification. Using a static binary rewriter, we evaluate the performance impact of the proposed compiler techniques on the SPEC2006 integer benchmarks for two intensive monitoring tasks: taint-propagation and memory bug detection. Comparing to instrumentation-based monitors, the proposed techniques can bring down the performance overhead of the two monitors from 10.6× and 9.0× to 2.36× and 2.17×, respectively.

No abstract available.

Land surface temperature (LST) retrieval has always been a key issue in the thermal infrared remote sensing research area. Landsat5 TM data with a higher spatial resolution thermal infrared band of 120 m was often used to retrieve land surface temperature. However, the fact that Landsat 5 possesses only one thermal infrared band is also a critical limitation for LST retrieval, which does not allow applying a split-window method. LST retrieval from the radiative transfer equation using in situ radiosounding data is often not practical because of the scarcity of in situ radiosounding data. Therefore in most cases, only at-satellite brightness temperature was obtained from TM6 data, which is far different from the land surface temperature. Hence the precision of land surface temperature retrieval was actually not so satisfied. While the proposal of the generalized single-channel algorithm in 2003 makes it possible to figure out land surface temperature from TM6 data with high precision. Based on this algorithm, a test for land surface temperature retrieval of Beijing region was carried out with Landsat5 TM data acquired on 6 May 2005. MODIS data received on the same date was used to compute the total atmospheric water vapor content which is necessary for the algorithm. Furthermore, the retrieving result has been validated using simultaneously measured in situ data. A significantly high precision with a root mean square deviation (rmsd) of 1.67 K has been achieved by the approach introduced in this paper, which shows the advantages of synthetically utilizing multi-satellite data.

Wavelet and Fourier analyses are widely used in various fields of image processing. They can be utilized in land use remote sensing dynamic monitoring to detect change information automatically. The procedures are as followed: firstly do wavelet transform or Fourier transform to each band or principal component of the multi-spectrum images of two different periods, the coefficients after transform are subtracted, then do inverse transform. The result images display land using changing areas with high brightness. In this paper, we do experiments using the two transforms, and find the result of wavelet transform is better.