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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 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.

Street view image classification aiming at the urban land use analysis is difficult because the class labels (e.g., commercial area) are concepts with higher abstract levels compared to the ones of general visual tasks (e.g., persons and cars). Therefore, classification models using only visual features often fail to achieve satisfactory performance. In this article, a novel approach based on a “bottom-up and top-down” framework is proposed. Instead of using visual features of the whole image directly as common image-level models based on convolutional neural networks (CNNs) do, the proposed framework first obtains low-level semantic, namely, the bounding boxes of buildings in street view images through a bottom-up object discovery process. Their contextual information, such as the co-occurrence patterns of building classes and their layout, is then encoded into metadata by the proposed algorithm “Context encOding of Detected buildINGs” (CODING). Finally, these metadata (low-level semantic encoded with context information) are abstracted to high-level semantic, namely, the land use label of the street view image through a top-down semantic aggregation process implemented by a recurrent neural network (RNN). In addition, in order to effectively discover low-level semantic as the bridge between visual features and higher abstract concepts, we made a dual-labeled data set named “Building dEtection And Urban funcTional-zone portraYing” (BEAUTY) of 19070 street view images and 38857 buildings based on the existing BIC_GSV. The data set can be used not only for street view image classification but also for multiclass building detection. Experiments on “BEAUTY” show that the proposed approach achieves a 12.65% performance improvement on macroprecision and 12% on macrorecall over image-level CNN-based models. Our code and data set are available athttps://github.com/kyle-one/Context-Encoding-of-Detected-Buildings/.

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.