Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR‐PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR‐PANet is used as the neck network. After the upper sampling, depthwise separable convolution is used to greatly avoid the lack of small target feature information in the convolution of the shallow network and increase the semantic information in the high‐level network. Finally, Gamma correction is used to preprocess the image before image training, which effectively reduces the interference of shadow and other factors on training. The experiment proves that the method has a better effect on small targets obscured by shadows and under the color similar to the background of the picture, and the accuracy is significantly improved based on the original algorithm.

Parallel computing is a common method to accelerate remote sensing image processing. The paper briefly describes six commonly used interpolation functions and studies three commonly used parallel computing methods of the corresponding nine interpolation algorithms in remote sensing image processing. Firstly, two kinds of general parallel interpolation algorithms (for CPU and GPU respectively) were designed. Then, in two typical application scenarios (data-intensive and computing-intensive), four computing methods (one serial method and three parallel methods) of these interpolation algorithms were tested. Finally, the acceleration effects of all parallel algorithms were compared and analyzed. On the whole, the acceleration effect of parallel interpolation algorithm is better in computer-intensive scenario. In CPU-oriented methods, the speedup of all parallel interpolation algorithms mainly depends on the number of physical cores of CPU, while in GPU-oriented methods, a speedup is greatly affected by the computation complexity of an algorithm and the application scenario. GPU has a better acceleration effect on the interpolation algorithms with bigger computation complexity, and has more advantages in the computing-intensive scenarios. In most cases, GPU-based interpolation is ideal for efficient interpolation.

Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD‐DataSet and NWPU‐DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.

Nitrogen oxides (NOx) are known to be irritant gases, which present considerable risks to human health. TROPOMI NO₂ vertical column density (VCD) is commonly employed to estimate NOx emissions through the integration of complex models. However, satellite data often suffer from incompleteness, hindering the ability to achieve long-term and comprehensive estimates. In this study, we propose a reconstruction method to achieve comprehensive coverage of NO₂ VCD in China by leveraging the relationship between satellite data and meteorological variables. In addition, the CNN-BiLSTM-ATT model was developed to estimate China’s monthly NOx emissions from 2021 to 2023 in combination with other ancillary data, such as ERA5 meteorological data, topographic data, and nighttime light data, achieving a correlation coefficient (R) of 0.83 and a root mean squared error (RMSE) of 9.05 tons (T). The factors influencing NO₂ VCD were assessed using SHAP values, and the spatiotemporal characteristics and density distribution of NOx emissions were analyzed. Additionally, annual emission trends were evaluated. This study offers valuable insights for air quality management and policymaking, contributing to efforts focused on mitigating the adverse health and environmental impacts of NOx emissions.