Atmospheric correction and cloud removal are core tasks of the atmospheric radiative transfer model in satellite remote sensing radiometric calibration. Accurate atmospheric correction and cloud removal of satellite remote sensing images are critical preliminary steps for effective satellite remote sensing applications. Current methods for atmospheric correction and cloud removal in satellite remote sensing imagery are mainly divided into traditional algorithms and deep learning methods. Traditional remote sensing algorithms typically involve complex atmospheric radiative transfer models, intricate meteorological parameters, synchronized ground measurements and corresponding geographic data. There are significant difficulties and limitations in achieving detailed descriptions of ground and atmospheric models. Deep learning methods typically employ computer vision techniques. These methods cannot effectively utilize atmospheric parameters and multispectral image features. Therefore, they cannot significantly improve the data quality of spectral satellite remote sensing images. This study integrates multi-temporal and multi-spectral remote sensing images, atmospheric data (such as aerosol optical thickness and water vapor), and remote sensing data like surface reflectance. A novel unified model is proposed to handle atmospheric correction and cloud removal for multi-spectral satellite remote sensing images. The unique geographical environment and diverse and complex atmospheric conditions of the Guilin region make its satellite remote sensing data rich in research characteristics for atmospheric correction and cloud removal tasks. It can provide a typical and representative experimental scenario for this study. This study first utilized Sentinel-2 top-of-atmosphere and surface reflectance data from the Guilin region to create four datasets: true color and multispectral fusion datasets with atmospheric correction incorporating aerosol parameters, single and multi-temporal cloud removal datasets. Then, we developed a conditional guided diffusion model. This model can capable for both multispectral atmospheric correction and multi-temporal cloud removal tasks by redesign the conditional control and encoding sections. Our method achieved satisfactory results in both true color and multispectral atmospheric correction tasks. For atmospheric correction tasks on true color images, our method achieved MAE, SSIM, and PSNR metrics of 0.0125, 0.9445, 35.5221 dB. For the atmospheric correction task in multispectral images, the method achieved MAE, SSIM, and PSNR metrics of 0.0174, 0.9499, and 36.3547 dB. For multi-temporal true color cloud removal tasks, the method achieved MAE, SSIM, and PSNR metrics of 0.0224, 0.9556, and 36.2122 dB.

Image-based line segment extraction plays an important role in a wide range of applications. Traditional line segment extraction algorithms focus on the accuracy and efficiency, without considering the integrity. Serious line segmentation fracture problems caused by image quality will result in poor subsequent applications. To solve this problem, a multi-constrained line segment extraction method, based on multi-scale image space, is presented. Firstly, using Gaussian down-sampling with a classical line segment detection method, a multi-scale image space is constructed to extract line segments in each image scale and all line segments are projected onto the original image. Then, a new line segment optimization and purification strategy is proposed with the horizontal and vertical distances and angle geometric constraint relationships between line segments to merge fracture line segments and delete redundant line segments. Finally, line segments with adjacent positions are optimized using the grayscale constraint relationship, based on normalized cross-correlation similarity criterion for realizing the second optimization of fracture line segments. Compared with mainstream line segment detector and edge drawing lines methods, experimental results (i.e., indoor, outdoor, and aerial images) indicate the validity and superiority of our proposed methods which can extract longer and more complete line segments.

Guilin is situated in the southern part of China with abundant rainfall. There are 137 reservoirs, which are widely used for irrigation, flood control, water supply and power generation. However, there has been a lack of systematic and full-coverage remote sensing monitoring of reservoir dams for a long time. According to the latest public literature, high-resolution unmanned aerial vehicle (UAV) remote sensing has not been used to detect changes on the reservoir dams of Guilin. In this paper, an intelligent segmentation change detection method is proposed to complete the detection of dam change based on multitemporal high-resolution UAV remote sensing data. Firstly, an enhanced GrabCut that fuses the linear spectral clustering (LSC) superpixel mapping matrix and the Sobel edge operator is proposed to extract the features of reservoir dams. The edge operator is introduced into GrabCut to redefine the new energy function’s smooth item, which makes the segmentation results of enhanced GrabCut more robust and accurate. Then, through image registration, the multitemporal dam extraction results are unified to the same coordinate system to complete the difference operation, and finally the dam change results are obtained. The experimental results of two representative reservoir dams in Guilin show that the proposed method can achieve a very high accuracy of change detection, which is an important reference for related research.

Line matching is the foundation of three-dimensional (3D) outline reconstruction for city buildings in aerial photogrammetry. Many existing studies have good line matching effects when dealing with aerial images with short baselines and small viewing angles. However, when faced with wide-baseline and large viewing-angle images, the matching effect drops sharply or even fails altogether. This paper deals with an efficient and simple method to achieve better line matching performance by a pair of wide-baseline aerial images, which make use of viewpoint-in variance to conduct line matching in rectified image spaces. Firstly, the perspective transformation relationship between the image plane and the geoid plane can be established from a Positioning and Orientation System (POS). Then, according to perspective projection matrices, two original images are separately rectified to conformal images, whose perspective deformation of large viewing-angle can be eliminated. Finally, the rectified images are used to conduct line matching, and the matched line segments obtained are back-projected to the original images. Four pairs of urban oblique aerial images are used to demonstrate the validity and efficiency of this method. Compared with line matching on original images, the number and the correctness of the matched line segments are greatly improved. Moreover, there is no loss of time efficiency. The proposed method can also be applied to general UAV (Unmanned Aerial Vehicle) aerial photogrammetry and introduced into matching for other geometric features, such as points, circles, curves, etc.

Black carbon (BC) aerosols have a considerable impact on humans because they not only cause environmental pollution and reduce visibility but also harm human health. During the heating season in northern China, a large amount of coal is burned for heating, producing a large amount of BC. There are few studies on BC properties during the heating season. In this paper, BC is measured optically, so it is referred to as equivalent black carbon (EBC). This paper investigated EBC properties in depth during the heating and nonheating seasons of a typical urban environment in China with two years of EBC measurements. The results show that: (1) EBC aerosol concentrations during the heating season were significantly higher than those during the nonheating season. (2) The main sources of EBC aerosols throughout the year are liquid sources. During the heating season, solid sources (coal and biomass combustion) are dominant. (3) The proportion of brown carbon (BrC) produced by biomass energy during the heating season is greater than that during the nonheating season. (4) The resulting backward trajectory indicates that a large portion of the high EBC aerosol concentration sources originate from northern and northwestern China. Our results reveal that the characteristics and sources of EBC in the urban environment of northern China vary widely, suggesting that different measures should be taken to reduce BC aerosol concentrations during heating and nonheating seasons.