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.

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.

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.

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.

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.

In remote sensing community, accurate image registration is the basement of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. But the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring off the border of image by weighting window function with reference and sensed image. However, the approach also inevitably filters out non-border information of an image, which is useful to image registration based on phase correlation. In this paper, another way of thinking to eliminate the effect of image border is proposed, namely decomposing the image into two images, one is periodic image and the other is smooth image. Engulfing the original image by the periodic one has no direct effect on the image border due to applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. The novel algorithm of eliminating the image border can improve the success rate and registration accuracy of phase correlation based image registration. To illustrate its superiority, we showed the corresponding magnitude of Fourier Transform of image visually and compared two measurements with other three state-of-the-art algorithms quantitatively.

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) .