Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for relative radiometric calibration. One technique, often called side-slither, where the sensor axis is rotated 90° in yaw relative to normal acquisitions, has been gaining popularity, being applied to Landsat 8, QuickBird, RapidEye, and other satellites. Side-slither can be more time efficient than some of the traditional methods, as only one acquisition may be required. In addition, the side-slither does not require any onboard calibration hardware, only a satellite capability to yaw and maintain a stable yawed attitude. A relative radiometric calibration method based on histograms of side-slither data is developed. This method has three steps: pre-processing, extraction of key points, and calculation of coefficients. Histogram matching and Otsu’s method are used to extract key points. Three datasets from the Chinese GaoFen-9 satellite were used: one to obtain the relative radiometric coefficients, and the others to verify the coefficients. Root-mean-square deviations of the corrected imagery were better than 0.1%. The maximum streaking metrics was less than 1. This method produced significantly better relative radiometric calibration than the traditional method used for GaoFen-9.

Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based on coarse-to-fine strategy and geometric scale-invariant feature transform (SIFT). In the coarse matching step, a robust matching method scale restrict (SR) SIFT is implemented at low resolution level. The matching results provide geometric constraints which are then used to guide block division and geometric SIFT in the fine matching step. The block matching method can overcome the memory problem. In geometric SIFT, with area constraints, it is beneficial for validating the candidate matches and decreasing searching complexity. To further improve the matching efficiency, the proposed matching method is parallelized using OpenMP. Finally, the sensing image is rectified to the coordinate of reference image via Triangulated Irregular Network (TIN) transformation. Experiments are designed to test the performance of the proposed matching method. The experimental results show that the proposed method can decrease the matching time and increase the number of matching points while maintaining high registration accuracy.

During 1995–2010, a comparison of the comprehensive effects of four different types of urban roads on landscape diversity in Shanghai was performed. Remote sensing images from 1995, 2000, 2005, and 2010 and the transfer matrix mathematical model were used to conduct the research, which focused on studying the rule of land use change in the study area over the past 15 years. Seven different landscape indices (SHDI, SHEI, ED, NP, TE, AWMSI, and MSI) were also selected to analyze the landscape pattern of Shanghai in three different periods and to explore the regularity of its dynamic changes and influential factors. Buffer analysis and statistical analyses were applied to represent the relationship between the distance from different roads quantitatively (e.g., railway, highway, national highway, and provincial highway) and a series of landscape pattern metrics (e.g., SHDI, PD, ED, and IJI) thus exploring the influence of transportation routes on the surrounding ecological environment. The results showed that (1) each type of landscape in the study area changed considerably during the research periods, a great amount of cultivated land was transformed into construction land, and the water area largely decreased. (2) The transportation routes exerted different impacts on the road landscape during different periods, and the effects were more obvious at the early stage of road construction. (3) The national way and the highway influenced the patch density (PD) index and edge density (ED) index more clearly, while the national way and the railway had greater effects on the landscape diversity indices. This study presents an approach that can be applied to quantitatively describe the impacts of transportation routes on landscape patterns and has the potential to facilitate route network planning.

The advancement of remote sensing technology and high-resolution sensors has led to a significant improvement in the spatial resolution of remote sensing images, resulting in the capture of more abundant image texture details. This has created a robust data foundation for remote sensing image interpretation. Semantic segmentation is a critical technology in remote sensing image interpretation, as it enables pixel-by-pixel classification of objects in images. Due to its advanced classification capabilities, semantic segmentation has found wide application in many fields, such as landslide monitoring,^1,2 plant disease monitoring,³ and water quality monitoring,⁴ in the field of disaster monitoring. In urban planning, it has been utilized for road detection,⁵ building change detection,⁶ illegal building detection,⁷ 3D scene reconstruction.^8,9 In the field of land cover mapping,¹⁰ it has been used to detect forest land, agricultural land, bare land, irrigated land, and dry land. As the classification of ground objects becomes increasingly refined, the demand for more advanced and sophisticated classification and segmentation algorithms has significantly increased.

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management.

Ratio transformation methods are widely used for image fusion of high-resolution optical satellites. The premise for the use the ratio transformation is that there is a zero-bias linear relationship between the panchromatic band and the corresponding multi-spectral bands. However, there are bias terms and residual terms with large values in reality, depending on the sensors, the response spectral ranges, and the land-cover types. To address this problem, this paper proposes a panchromatic and multi-spectral image fusion method based on the panchromatic spectral decomposition (PSD). The low-resolution panchromatic and multi-spectral images are used to solve the proportionality coefficients, the bias coefficients, and the residual matrixes. These coefficients are substituted into the high-resolution panchromatic band and decompose it into the high-resolution multi-spectral bands. The experiments show that this method can make the fused image acquire high color fidelity and sharpness, it is robust to different sensors and features, and it can be applied to the panchromatic and multi-spectral fusion of high-resolution optical satellites.