Accurate cloud detection in remote sensing images constitutes a critical preprocessing requirement for ensuring the validity of subsequent analytical applications. Existing cloud detection methods effectively identify primary cloud regions but struggle with precise boundary delineation and distinguishing spectrally similar surfaces, particularly between clouds and terrain with comparable reflectance properties. To address these challenges, we propose the coarse-to-fine progressive network (CFPNet), a progressive detection framework integrating across channel and spatial dimensions. The framework incorporates two innovative components: a channel spatial attention residual fusion module (CSARF) and a multi-mask adaptive attention module (MMAA). The CSARF module achieves the network’s initial focus on clouds, while the MMAA module enables fine feature extraction of clouds. Specifically, The CSARF module employs channel attention (CAM) and spatial attention (SAM) mechanisms to suppress background noise while enhancing discriminative feature representation. MMAA employs muti-mask self-attention (MMSA) to compute channel self-attention and capture long-range dependencies, while using a multi-mask strategy to filter important channels. Deformable contextual feed-forward network (DCFN) then adaptively extracts cloud boundary features through deformable convolution, minimizing non-cloud pixel interference. Hence, the network enables coarse-to-fine feature extraction of clouds across both channel and spatial dimensions. Experimental results on the GF1-WFV, AIR-CD and Sentinel-2 datasets demonstrate that our method achieves superior performance in boundary detail accuracy and inter-class feature classification compared to other methods.

With the increasing number of spaceborne synthetic aperture radar (SAR) systems, traditional absolute radiometric calibration methods based on calibration fields are becoming increasingly difficult to meet the demand for long-term monitoring of radiometric accuracy in spaceborne SAR images. SAR radiometric cross-calibration methods based on the stability characteristics of big data are expected to solve this problem. In this study, a space-borne SAR radiometric cross-calibration method considering typical scattering effects in building areas was proposed by analyzing the imaging mechanism of SAR images of building areas. The experiment using Sentinel-1A/-1B data has verified that extracting stable pixels based on this method can effectively improve the stability of the calibration benchmark, and it has been determined that the mean centroid of building areas based on the sliding window is a more stable calibration benchmark. The stability of this calibration benchmark is 0.23 dB, which is more stable than that of tropical rainforests; In addition, a SAR radiometric cross-calibration scheme was designed, and experiments were conducted based on this scheme. The experimental results showed that the absolute calibration accuracy of cross-calibration on the same satellite was 0.25 dB, and that of cross-calibration on different satellites was 0.36 dB.

Abstract Current dehazing networks usually only learn haze features in a single‐image colour space and often suffer from uneven dehazing, colour, and edge degradation when confronted with different scales of ground objects in the depth space of the scene. The authors propose a multimodal feature fusion image dehazing method with scene depth prior based on a decoder–encoder backbone network. The multimodal feature fusion module was first designed. In this module, affine transformation and polarized self‐attention mechanism are used to realize the fusion of image colour and depth prior feature, to improve the representation ability of the model for different scale ground haze feature in‐depth space. Then, the feature enhancement module (FEM) is added, and deformable convolution and difference convolution methods are used to enhance the representation ability of the model for the geometric and texture feature of the ground objects. The publicly available dehazing datasets are used for comparison and ablation experiments. The results show that compared with the existing classical dehazing networks, the peak signal‐to‐noise ratio (PSNR) and SSIM of the authors’ proposed method have been significantly improved, have a more uniform dehazing effect in different depth spaces, and maintain the colour and edge details of the ground objects very well.

The traditional auto-calibration of synthetic aperture radar (SAR) images based on the range-Doppler (RD) model couples the coordinates of ground target points (GTPs) and the slant range correction to solve, resulting in unstable solutions. This paper proposes a twofold positioning method (TPM) for multiview spaceborne SAR images to solve this problem. In order to obtain the more precise coordinates of GTPs and the stability of the solution, the conjugate gradient method (CGM) is performed to the initial and secondary positioning for the normalized RD model. Compared with the traditional least squares method (LSM), the accuracy of TPM is on average 13.47% higher with YaoGan-SAR satellite (150MHz and 24.4us), and 44.38% higher with YaoGan-SAR satellite (200MHz and 24.4us). In addition, the stability of the method is improved. The experimental results based on the YaoGan-SAR satellite images verify the effectiveness of the method.

GaoFen-3 (GF-3) is a C-band multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes. However, its initial positioning accuracy remains unsatisfactory, thereby hindering its use for large-area surveying and mapping. This study proposes a block orthorectification method without ground control points (GCPs) using the GF-3 Fine strip II (FSII) mode. To address the challenges with the accuracy and efficiency of this method, an integrated block orthorectification method was developed to conduct integrated processing of large-scale GF-3 satellite images without GCPs. Geometric calibration was used to improve the absolute positioning accuracy of each SAR image. Then, several tie points (TPs) were extracted using the SAR scale-invariant feature transform (SIFT) operator. A parallel matching strategy was used in the block images registration. The block adjustment model was constructed to solve the orientation parameter of all SAR images. The experimental results of 1,468 GF-3 images of China’s entire land area show a TPs root-mean-square error of 0.724 pixel and 8.014 m for the independent checkpoint, suggesting that the proposed method can effectively improve the geometric accuracy of GF-3 satellite images and demonstrate the feasibility of large-scale SAR mapping without GCPs.

High-resolution synthetic aperture radar (SAR) satellites have become an important way to observe the earth. However, the geometric positioning accuracy of SAR satellite images across different times and spaces is an important factor affecting the realization of global remote sensing applications. In this study, a multimode hybrid geometric calibration method that incorporates an atmospheric propagation delay correction and that can be used to detect systematic errors affecting the geometric positioning accuracy of SAR satellites is described. The spatiotemporal variation reasons of geometric positioning error sources for spaceborne SAR are then analyzed. Finally, the stability of geometric positioning accuracy is evaluated using the described method on data extracted from Yaogan-13 (YG-13) SAR satellite images with long time series and multiple test areas in China. The results reveal that during the study period (2015–2017), the geometric positioning accuracy of the YG-13 SAR system was relatively stable and is better than 3 m regardless of the spatial distribution, after removal of systematic pulse-dependent slant range errors and atmospheric correction. Furthermore, the validation results provide a reference for the design of SAR satellite systems, the establishment of calibration periods, and quantitative remote sensing application.

This article proposes a novel fusing filter algorithm based on a surface variation anisotropic diffusion (SVAD) filter and a ratio image filter to achieve good speckle reduction and edge preservation. The proposed algorithm can be divided into three steps. First, the proposed SVAD filter effectively calculates the diffusion coefficient of each pixel to obtain filtering results on different scales. Second, the proposed ratio image filter obtains a new denoising result that can effectively recover some details lost with the SVAD filter. Then, the two filtering results are fused to obtain the final despeckling result. Furthermore, the effects of the weighting coefficients of the fusion processing and the number of iterations of the ratio image filter on the final filtering results are analyzed. The proposed algorithm is effectively evaluated by conducting some experiments on the added noise image and real synthetic aperture radar (SAR) images. The experimental results confirm that the proposed method can not only significantly reduce speckle but also effectively preserve the edge information of images.

The most important part of frame rate up-conversion (FRUC) is block matching. The geometric properties of the image were not taken into consideration in traditional block matching algorithm, so the matching result of motion estimation cannot reach the optimal. A novel FRUC algorithm based on Bandelet was proposed in this paper. The algorithm includes: Firstly, a soft threshold Bandelet transform of matching block was performed. The optimal matching block was determined through detection of direction similarity and Bandelet coefficient similarity; secondly, vector median filtering (VMF) and overlapped block motion compensation (OBMC) were carried out by adopting motion vector to realize interpolated frame algorithm. Experimental results show that the FRUC algorithm based on Bandelet can further promote the quality of FRUC.