Growing demands in sustainable development and resource management are driving increasing reliance on remote sensing-based Earth observation and image interpretation. In parallel, multimodal collaborative processing is attracting research attention. Synthetic aperture radar (SAR) and optical images offer complementary advantages but pose challenges for simultaneous use due to platform constraints and environmental conditions, often leaving only one modality available and impeding joint analysis. Generative models, particularly generative adversarial networks (GANs) and diffusion models (DMs), address this by learning cross-modal mappings. Translated images preserve structure and semantics while adopting target characteristics, thereby facilitating collaborative use. This review systematically categorizes translation frameworks spanning GANs, DMs, and other generative models. It then details downstream tasks supported by SAR–optical translation, including cloud removal, change detection, semantic segmentation, registration, and object detection, highlighting how translation bridges data gaps and enhances interpretation robustness. Furthermore, we provide open-source code and public datasets, discuss current challenges, and outline future research directions.

The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved.

With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.

Analysis Ready Data (ARD) has been greatly recommended by the Committee on Earth Observation Satellites (CEOS) for simplifying and fostering long time series analysis at large scale with minimum additional user effort. Landsat ARD has been successfully made and widely used for large scale analysis. Subsequently, the Chinese satellite data similar to Landsat data have been processed and will be processed into ARDs to promote the use of the Chinese satellite data. At the first stage of the mission, the 4 Wide Field Viewing (WFV) data on GaoFen 1 (GF1) covering the whole of China and the surrounding areas have been processed into ARD. The ARD is provided as standard tiles under a common and unified projection with per pixel quality assurance and metadata for tracing back and further processing data, which are finally stored into a Hierarchical Data File (HDF); furthermore, all spectral bands are georegistered and radiometrically cross-calibrated as top of atmosphere (TOA) reflectance and are atmospherically corrected as surface reflectance (SR). Therefore, the ARD can be further used easily to produce land cover and land cover change maps and retrieve geophysical and biophysical parameters.

On 6 September 2008, two optical satellites, HJ-1 A and B (HJ-1 A/B), were successfully launched from China. However, the system geometric correction products of the HJ-1 A/B charge-coupled device (HJ-1 images) have low geometric precision and need to be corrected. The HJ-1 images have a large aspect angle, a wide swath width, and a large image size. Furthermore, the local geometric distortions are too complex in one scene. Given these characteristics of HJ-1 images, geometric correction is still a challenging work. This article proposes an automatic geometric precision correction system (GPCS) based on the automatic registration between HJ-1 images and Landsat Thematic Mapper images. First, the coarse image matching method based on geometric-restricted scale-invariant feature transform (SIFT) is used to determine the coarse global transformation between the HJ-1 image and the reference image. Second, inspired by the hierarchical method of non-rigid registration for medical images, a hierarchical image matching approach is proposed based on the combination of SIFT feature points and template matching. This approach decomposes a matching problem of a whole image into numerous matching problems of image blocks and can overcome the impact of local distortions in HJ-1 images. Hierarchical random sample consensus (RANSAC) based on digital elevation model(H-RANSAC) is used to remove incorrect control points. Third, an HJ-1 image is rectified using a triangulated irregular network. Finally, the automatic evaluation method based on automatic image matching between the corrected HJ-1 image and the reference image is adopted to evaluate the geometric precision. On the one hand, experiments on eight HJ-1 images demonstrate the efficiency and accuracy of the different steps of GPCS. On the other hand, experiments on 1000 HJ-1 images also demonstrated the robustness, accuracy, and suitability for batch processing.

Recent years, China has launched a series of remote sensing satellites, such as HJ-1, FY3, GF-1, etc. Making the extreme rapid growth of high resolution remote sensing images. In this case, preliminary data processing is becoming more and more important, dehazing is an important step of radiometric processing in prfeliminary data processing. However, the existing dehazing methods are quite slow. In this paper, we are going to discuss a faster dehazing algorithm based on large-scale median filtering, by using the multi-resolution remote sensing image data of GF-1 satellite, we can highly optimize the algorithm itself..Parallel computing techniques are used to improve the processing speed, and the proposed algorithm is implemented in standard C++. On the computer with Intel core i7-3770 and CPU 3.4 GHz, our algorithm can decrease the dehazing time from one hour to less than five minutes for a 4-band, 10000×10000 pixel size, 16 bits unsigned integer data type image.

The spatial consistency of low-resolution multi-source remote sensing data is of great importance for their combination in global change research. Currently, many methods are developed for the precise geometric correction of single kind of low-resolution data. However, the spatial consistency correction method is still need to be developed when many different kinds of low-resolution sensors' data are taken into consideration altogether, which is aimed to make their spectral data become consistent in geo-location. MODIS surface reflectance products, as they are of high accuracy of geo-location and data quality among low-resolution data, the spectral data of the multi-source low-resolution sensors are corrected to be consistency with it, which contain the level 1B data of NOAA/AVHRR, FY-3/VIRR, FY-3/MERSI, FY-2/VISSR. The proposed method in this paper can conducted spatial consistency correction on multi-source low-resolution remote sensing data precisely, efficiently, and automatically, which is based on contour point coarse matching and contour precise matching.