Landslide disasters have garnered significant attention due to their extensive devastating impact, leading to a growing emphasis on the prompt and precise identification and detection of landslides as a prominent area of research. Previous research has primarily relied on human–computer interactions and visual interpretation from remote sensing to identify landslides. However, these methods are time-consuming, labor-intensive, subjective, and have a low level of accuracy in extracting data. An essential task in deep learning, semantic segmentation, has been crucial to automated remote sensing image recognition tasks because of its end-to-end pixel-level classification capability. In this study, to mitigate the disadvantages of existing landslide detection methods, we propose a multiscale attention segment network (MsASNet) that acquires different scales of remote sensing image features, designs an encoder–decoder structure to strengthen the landslide boundary, and combines the channel attention mechanism to strengthen the feature extraction capability. The MsASNet model exhibited an average accuracy of 95.13% on the test set from Bijie’s landslide dataset, a mean accuracy of 91.45% on the test set from Chongqing’s landslide dataset, and a mean accuracy of 90.17% on the test set from Tianshui‘s landslide dataset, signifying its ability to extract landslide information efficiently and accurately in real time. Our proposed model may be used in efforts toward the prevention and control of geological disasters.

Accurate inversion of remote sensing indices images with high-resolution is an important basis for extracting the surface information on fine scale and the remote sensing application with high precision. Under the background that the effect of atmospheric correction on the inversion accuracy of medium-resolution remote sensing indices has been widely concerned, the atmospheric correction method based on the synchronous atmospheric parameters retrieved from the Synchronization Monitoring Atmospheric Corrector (SMAC), which is on board the High Resolution Multi-Mode Integrated Imaging satellite (Gao Fen Duo Mo, GFDM) platform, is proposed. The “Spaceborne - Airborne” synchronous imaging experiment was conducted to investigate the effect of synchronous atmospheric correction on the accuracy of high-resolution remote sensing indices images. The low-altitude synchronous remote sensing indices images were taken as the reference standard, the correlation between the NDVI and NDWI images after synchronous atmospheric correction and the reference images are improved by 4.27% and 12.22%, respectively. The mean errors in average NDVI and NDWI value of sampling plot decreased by 0.2489 and 0.1032, respectively. The average errors in the peak value of change rate of pixel index value are reduced by 0.1194 and 0.2288 in the case of NDVI and NDWI, respectively. Compared with the results of FLAASH, the synchronous atmospheric correction method shows better performance, especially in the targets with low reflectance (the atmospheric radiation contribution is higher than the surface radiation contribution), and in the adjacent area between two different ground objects. The results demonstrate that synchronous atmospheric correction can effectively improve the inversion accuracy of high-resolution remote sensing indices images and its ability of quantitative application.

Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications.

The adjacency effect, the contribution of the neighboring pixels to the radiance of the line of sight pixel, is caused by the Rayleigh scattering of atmospheric molecules and Mie scattering of aerosol particles. The adjacency effect will cause the reflectance of each pixel in the apparent reflectance satellite image to be between the real reflectance and the average background reflectance, reducing the accuracy of the surface reflectance inversion. Therefore, it is very important to remove the adjacency effect to improve the accuracy of retrieving the surface reflectance from satellite images. The most critical issue of the adjacency effect is to accurately calculate the weight of the contribution of each background pixel to the adjacency effect. The weight value of the contribution of each background pixel to the adjacency effect mainly depends on the spatial distance between the target pixel and the background pixel, the difference in reflectance between the target pixel and the background pixel, and the optical thickness of atmospheric molecules and the optical thickness of aerosol. At present, the commonly used weight function for calculating the weight value considers only the influence of optical thickness and spatial distance on the weight value. These weight functions are applied to a relatively uniform surface. However, when these weight functions are applied to an inhomogeneous surface, they will greatly reduce the accuracy of the adjacency effect correction. The combination of ground features in satellite images with the sub-meter spatial resolution is complex, so the influence of the difference in reflectance between the target pixel and the background pixel on the adjacency effect must be considered. The adaptive atmospheric correction algorithm proposed in this paper can adjust the weight value of the contribution of background pixels to the adjacency effect according to the spatial distance between the target pixel and the background pixel, the difference in reflectance between the target pixel and the background pixel, and the difference between the atmospheric molecules’ optical thickness and aerosol optical thickness. The adaptive atmospheric correction algorithm is used to correct the adjacency effect on GF-2 panchromatic satellite images. The results show that the adaptive atmospheric correction algorithm can effectively remove the adjacency effect in sub-meter spatial resolution optical satellite images, improve both the accuracy of quantitative study and the satellite image quality.

Accurately detecting the true spatial structure of exhaust plume density fields is important for optimising engine design, validating aerodynamic performance and advancing aircraft detection technologies. The fundamental difficulty in non-intrusive three-dimensional density field measurement lies in whether a density value at a specific point in free space can be mapped and whether its signal characteristics are practically observable. This paper uses the intensity of the light scattered instantaneously by an ultra-narrow laser pulse at a specific point within the flow field as an optical signal to characterise the density value at that point. On this basis, it proposes a non-intrusive method of measuring density and reconstructing the three-dimensional field for exhaust plume tomography using an active light source. A detection system was established and plume experiments on a small turbofan engine were performed, and cross-sectional images of absolute density distribution along the central axis of the plume were obtained with a spatial resolution of 3.75 cm. The experimental results revealed a hollow three-dimensional structure, wherein the gaseous medium of the small turbofan exhaust plume expands along the central axis and undergoes compression at the surrounding regions. The maximum density recorded was 3.90 kg/m³, which is 218.90% higher than the background mean. The minimum recorded density was 0.93 kg/m³, which is 24.04% lower than the background mean. The fluctuation level, or coefficient of variation, reached 44.05%, which is 14.4 times higher than the environmental background. This method provides a non-intrusive means of measuring flow field structures, serving fundamental research on complex fluids, diagnostic testing of aeroengines, and related engineering applications.

Accurately detecting the true spatial structure of exhaust plume density fields is important for optimising engine design, validating aerodynamic performance and advancing aircraft detection technologies. The fundamental difficulty in non-intrusive three-dimensional density field measurement lies in whether a density value at a specific point in free space can be mapped and whether its signal characteristics are practically observable. This paper uses the intensity of the light scattered instantaneously by an ultra-narrow laser pulse at a specific point within the flow field as an optical signal to characterise the density value at that point. On this basis, it proposes a non-intrusive method of measuring density and reconstructing the three-dimensional field for exhaust plume tomography using an active light source. A detection system was established and plume experiments on a small turbofan engine were performed, and cross-sectional images of absolute density distribution along the central axis of the plume were obtained with a spatial resolution of 3.75 cm. The experimental results revealed a hollow three-dimensional structure, wherein the gaseous medium of the small turbofan exhaust plume expands along the central axis and undergoes compression at the surrounding regions. The maximum density recorded was 3.90 kg/m³, which is 218.90% higher than the background mean. The minimum recorded density was 0.93 kg/m³, which is 24.04% lower than the background mean. The fluctuation level, or coefficient of variation, reached 44.05%, which is 14.4 times higher than the environmental background. This method provides a non-intrusive means of measuring flow field structures, serving fundamental research on complex fluids, diagnostic testing of aeroengines, and related engineering applications.