The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.

High-resolution multispectral images are of great value in various fields. However, the physical limitations of satellite sensors hinder the simultaneous acquisition of high spatial resolution and high spectral resolution images. Deep learning has become a powerful tool for remote sensing image fusion, but its full potential has not been fully utilized. In order to maximize the quality of the high-resolution multispectral images generated by the improved model, this paper proposes a module called EBIFusion, which introduces an error backtracking mechanism to improve fusion performance. The module uses the intermediate results of the deep learning model to capture the information lost during the high-resolution image generation process, thereby guiding the optimization of model training. The experimental results on the GF-6 dataset show that the QNR index is increased by 2.22% after the introduction of the module. In addition, the spatial and spectral quality has been continuously improved on multiple datasets, including QuickBird and GF-2. The optimized models, such as PSGAN, PNN, GPPNN, and UCGAN, show stronger spatial details and spectral fidelity. The main contribution of this paper is to propose an error backtracking framework for recovering and compensating for the lost information in the process of remote sensing image fusion based on deep learning. Based on this, a lightweight and model-independent enhancement module EBIFusion is designed, which can be integrated into different deep learning fusion architectures. At the same time, the generalization ability of the module in multiple datasets and network paradigms is verified. In summary, the error backtracking module enhances the quality of the generated high-resolution multispectral images. In addition, it is not limited to specific models and data and can be used as the basis for a versatile and effective optimization component to improve the availability of high-resolution multispectral images.

Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation.